Research


  1. Unmanned Air System Departure Resistance Using Nonlinear Two-Time Scale Tracking Control

    Northrop-Grumman Corporation
    13 December 2011 - 31 January 2012
    Total award $16,713

    Unmanned Air Systems (UASs) are routinely restricted to fly in low angle-of-attack flight regimes where the vehicle dynamics are predominately linear. While this restriction helps ensure survivability of the vehicle by avoiding nonlinear flight regimes which can lead to departure from controlled flight, or nonlinear regimes where precise tracking of trajectories or aircraft states can be difficult if not impossible, it also restricts both routine operation and mission flexibility. A motivating example is the approach flight phase to precision landing, such as an arrested landing on an aircraft carrier. In this situation an aircraft must track both fast states (angular rates and sink rate) and slow states (flight path and heading) simultaneously, and accurately and reliably. Flying at higher approach speeds and therefore lower angles-of-attack can largely mitigate this two-time scale dynamics effect and prevent departure due to stall. But higher approach speeds have long been known to lead to higher occurrences of landing mishaps or accidents. Another motivating example is an aircraft tracking a prescribed fast moving target, while simultaneously regulating speed and/or one or more kinematic angles.

    This work develops nonlinear approach & landing control laws for a UAS that accomplish global tracking of both fast and slow states, using our recent results in geometric singular perturbation methods. The objective is to reduce the approach speed while accurately tracking flight path and velocity. The approach has been applied to simultaneously tracking both fast and slow variables for a desired reference trajectory that requires the aircraft to fly between linear and nonlinear flight regimes. The control laws were designed and implemented without making any assumptions about the specific nonlinearity of the 6-DOF aircraft model. Nonlinear simulation results we generated for a combined longitudinal lateral/directional maneuver of an F/A-18A Hornet, consisted of an aggressive vertical climb with a pitch rate of 25 deg/sec, followed by a roll at a rate of 50 deg/sec, all the while maintaining zero sideslip angle. The controller accomplished global asymptotic tracking while keeping all closed-loop signals bounded and well behaved.

    Extensions to the work in a subsequent phase will consist of verification flight testing of the controller, using the Pegasus research UAS owned and operated by the Vehicle Systems & Control Laboratory.

    Working with me on this program is Graduate Research Assistant:

  2. Intelligent Motion Video Algorithms for Unmanned Air Systems, Phase III

    Raytheon Company, Intelligence and Information Systems
    1 December 2011 - 31 December 2012
    Total award $200,000

    Advanced development and testing phase of the autonomous target tracking algorithms developed during the Phase I & II efforts (described below).

    TECHNICAL OBJECTIVES

    1. HARDWARE INTEGRATION: Sensor and experimental controller integration, flight controller off-board control modifications, validate integrated sensor and flight computer with Pegasus UAS via hardware-in-loop simulation. Validate experimental controller in flight test.
    2. ALGORITHM DEVELOPMENT: Perform additional learning for more complicated target paths, flight test reinforcement learning controller.
    3. GROUND STATION: Write extensions to ground station to transmit experimental controller commands back to vehicle. Ground test to ensure proper operation including failure scenarios in sensor, telemetry links, ground station PC, etc.
    4. FLIGHT VEHICLES: Build three additional Pegasus UAS vehicles.

    Working with me on this program are Graduate Research Assistants:

    • Kenton Kirkpatrick, Ph.D. student
    • Jim May, M.S. student
    • Drew Beckett, M.S. student
    • Grant Atkinson, M.S. student
    • Jim Henrickson, M.S. student
    • Tim Woodbury, M.S. student
    • Nick Oliviero, B.S. student
    • Josh Harris, B.S. student
  3. Novel Head Worn Displays and Interfaces for Pilots, Phase I

    Boeing Research & Technology
    1 September - 21 December 2011
    Total award $52,092

    Head Up Displays (HUD) are ubiquitous in high performance military aircraft because of the improved situational awareness that they provide to the pilot, which directly impacts mission effectiveness and flight safety. Transitioning of HUD technology to the Commercial Aircraft has been underway for more than a decade. While the situational awareness and flight safety improvements have clearly justified their use in this sector, the weight, volume, and power penalties they incur from a design and operations standpoint are not acceptable. Helmet Mounted Displays (HMD) used in current high performance military aircraft such as the F-22 and F-35 are advantageous from the weight, volume, and power standpoints, but they are not an acceptable solution for commercial flight operations since these pilots do not wear helmets. Head Worn Displays (HWD) that project HUD type data and information onto modified sunglasses or even a monocle offer a promising solution for the commercial aircraft sector. However, many human factors and systems level problems remain to be solved.

    The Phase I effort encompasses a complete systems engineering effort that includes pre-concept design, trade studies, options, and layout.

    TECHNICAL OBJECTIVES

    1. Determine a preferred system concept for a Head Worn Display (HWD) system to be used by pilots of FAR 23 and FAR 25 commercial air transports. The HWD would present a to be determined set of real-time situational awareness information consisting of weather (WX), traffic (TX), route, Traffic Collision Avoidance (TCAS), etc. A collision prediction and projection capability is also desired.
    2. Implement system into hardware and software prototype. Conduct human factors evaluation with test subject pilots via real-time simulation, using the Engineering Flight Simulator in the Vehicle Systems & Control Laboratory. Transition prototype system to Boeing, and assist Boeing engineers with development.

    Working with me on this program are Graduate Research Assistants:

    • Kenton Kirkpatrick, Ph.D. student
    • Jim May, M.S. student
    • Nick Oliviero, B.S. student
    • Josh Harris, B.S. student
  4. Machine Learning Control of Nonlinear, High Dimensional, Reconfigurable Systems

    Air Force Office of Scientific Research
    1 July 2011 - 30 June 2014
    Co-P.I. Dr. Suman Chakravorty
    Total award $420,000

    Optimal Control is the most general framework for posing and solving sequential decision making problems. Much progress has been made in solving such problems for deterministic systems and very efficient transcription based techniques, where the original infinite dimensional optimization problem is approximated by a finite dimensional nonlinear programming problem. For instance, the pseudo-spectral methods have been devised to solve open loop optimal control problems, with and without constraints. Unfortunately, the same cannot be said about problems under uncertainty. If we assume a stochastic model of uncertainty in the system process model, the sequential optimization problem can be posed as a so-called Markov Decision Problem (MDP) whose solution is given by a stochastic Dynamic Programming (DP) equation.

    However, it is also very well known that solutions to the DP problem are subject to Bellman's famous Curse of dimensionality, i.e. the fact that solution complexity grows exponentially in the dimension of the state-space. This makes solutions to the stochastic DP problem for continuous state and control spaces in particular, tractable only in low dimensional state-spaces, even given the computational resources available today. Moreover, to the best of our knowledge, it is very difficult to consider constraints on such continuous state/control space MDPs. There is also a need to consider the extension of the MDP techniques to multi-agent sequential optimization problems where the control computations for the individual agents need to take place in a collaborative and decentralized fashion, given DoD's increasing interest in such highly decentralized networked control systems. In addition, if there is sensing uncertainty in the system state, the sequential optimization problem transforms into the so-called "Partially Observed Markov Decion Problem (POMDP)", whose solution is given by an infinite dimensional Information Space DP problem which is virtually intractable for continuous state-space problems.

    Furthermore, we have previously developed a theory of Reinforcement Learning or Approximate Dynamic Programming (ADP) combined with Adaptive Control holds the promise to be effective for controlling various aerospace systems of interest, but has been developed to date for only a specialized class of dynamical systems. We are currently extending this approach to a much more realistic class of dynamical systems.

    TECHNICAL OBJECTIVES

    1. Extend ADP techniques to control of nonlinear, multiple time scale, non-affine systems in an Adaptive Control framework
    2. Develop solution techniques for MDPs that scale to continuous state and control spaces with constraints
    3. Extend MDP techniques to solve multi-agent co-ordination and control problems in a decentralized fashion.
    4. Develop solution techniques that scale to continuous state-space POMDP and their multi-agent generalizations.

    SELECTED PUBLICATIONS ON THIS TOPIC

    • Lampton, Amanda, and Valasek, John, "Multiresolution State-Space Discretization for Q-Learning with Pseudo-Randomized Discretization," Journal of Control Theory and Applications, Volume 9, Number 3, 2011, pp. 431-439.
    • Siddarth, Anshu, and Valasek, John, "Kinetic State Tracking for a Class of Singularly Perturbed Systems," Journal of Guidance, Control, and Dynamics, Volume 34, Number 3, May-June 2011, pp. 734-749.
    • Lampton, Amanda, Niksch, Adam, and Valasek, John, "Reinforcement Learning of a Morphing Airfoil-Policy and Discrete Learning Analysis," Journal of Aerospace Computing, Information, and Communication, Volume 7, Number 8, August 2010, pp. 241-260.
    • Kirkpatrick, Kenton, and Valasek, John, "Reinforcement Learning for Characterization of Hysteresis Behavior in Shape Memory Alloys," Journal of Aerospace Computing, Information, and Communication, Volume 6, Number 3, March 2009, pp. 227-238.
    • Lampton, Amanda, Niksch, Adam, and Valasek, John, "Reinforcement Learning of Morphing Airfoils with Aerodynamic and Structural Effects," Journal of Aerospace Computing, Information, and Communication, Volume 6, Number 1, January 2009, pp. 30-50.
    • Valasek, John, Doebbler, James, Tandale, Monish D., and Meade, Andrew J., "Improved Adaptive-Reinforcement Learning Control for Morphing Unmanned Air Vehicles," IEEE Transactions on Systems, Man, and Cybernetics: Part B, Volume 38, Number 4, August 2008, pp. 1014-1020.

    Working with me on this program are Graduate Research Assistants:

  5. Intelligent Motion Video Algorithms for Unmanned Air Systems, Phase II

    Raytheon Company, Intelligence and Information Systems
    1 June 2011 - 31 August 2011
    Total award $45,000

    This project will conduct a realistic outdoor flight test demonstration of the autonomous target tracking algorithm developed in the Phase I effort (described below).

    The flight vehicle will be the Pegasus fixed-wing Unmanned Air System (UAS) designed, built, and developed by the Vehicle Systems & Control Laboratory. Pegasus has a maximum takeoff weight of 60 lbs, a payload weight of 20 lbs, and one hour flight endurance.

    All flights will be conducted at the runway complex at the Flight Mechanics Laboratory, Texas A&M University Riverside Campus.

    Working with me on this program are Graduate Research Assistants:

  6. Intelligent Motion Video Algorithms for Unmanned Air Systems, Phase I

    Raytheon Company, Intelligence and Information Systems
    1 August 2010 - 31 January 2011
    Total award $45,000

    Advances in unmanned flight have led to the development of Unmanned Air Systems (UASs) that are capable of carrying state-of-the-art video capturing systems for the intended purpose of surveillance and tracking. UASs have the capability to fly through a target area with a mounted camera and allow humans to operate both the UAS and the camera to attempt to survey any objects that are deemed targets. These systems have worked well when controlled by humans, but having them operate autonomously is more challenging.

    One way to introduce the concept of autonomy to this problem is to determine a control policy that is capable of controlling the UAS autonomously along a certain trajectory while having the camera controlled by a human. Another way is to do the opposite, and have the UAS flown manually while the camera gimbals to capture and track identified targets. Both of these methods have been explored before and have merit, but having both the UAS and the camera operated autonomously could provide greater flight and tracking efficiency. Having a system that is capable of controlling a UAS and camera system to keep a selected target visible in the camera screen would free the human supervisor to focus on selecting viable targets and analyzing the images received.

    The biggest challenge stems from the need to determine an optimal control policy for keeping the target in the middle of the image. Conventional control techniques require determining an appropriate cost function and then finding the weights that make the control optimal. Although finding the optimal control is often straightforward, determining the cost function that best describes the problem is not straightforward. For this research, Reinforcement Learning (RL) is utilized for the determination of the optimal control policy that will both gimbal the camera and steer the UAS to provide target tracking.

    The specific RL algorithm used is Q-Learning with Adaptive Action Grid (AAG), developed by Lampton and Valasek as a means to provide greater accuracy in reaching the goal state (i.e., the target), while also decreasing the size (dimensions) of the state-space to be considered. This dramatically decreases the total number of states in the system, so that the learning time becomes more feasible and the storage requirements more tractable. The objective of the approach is to bring any target located in an image captured by a camera into the center of the image, using the AAG learned control policy described above. The learning agent will determine offline (initially) how to control the UAS and camera to get a target from any point in the image to the center and hold it there. A feature of this approach is that the learning agent will continue to learn and refine and update the previously offline learned control policy, during actual operation.

    Working with me on this program are Graduate Research Assistants:

  7. Optimal Six Degree-of-Freedom Maneuver Command Generator and Simulator Tools for Aircraft

    L-3 Communications, Integrated Systems
    15 February - 31 December 2009
    Total award $89,000

    The proposed work seeks to conduct applied research to support current and future L-3 IS goals in the area of trajectory design and generation. The focus of this work will be on investigating and implementing practical techniques for designing control inputs for complex aircraft trajectories, with an emphasis on evaluating dynamic loads and system identification. The objectives of the proposed effort are three fold. The first objective is to develop a MATLAB/SIMULINK flight simulation framework applicable to conventional transport airplane types. The second objective is to develop a simulated pilot that can be used for performing maneuvering flight and handling qualities analyses. The third objective is to develop methods for system identification using flight test data. Establishing a structured framework for aircraft data that will provide for modeling of a variety of transport aircraft types will facilitate new simulation developments. This effort will define an organized approach for defining, inputting and assembling the data needed for flight simulation.

    Working with me on this program is Graduate Research Assistant:

    • Jim May, M.S. student
  8. Optimal Trajectories and Analysis Using Implicit Functions

    NASA Johnson Space Center
    1 January 2009 - 31 May 2009
    Co-P.I. Dr. John L. Junkins
    Total award $51,913

    The project supports current and future NASA goals in the area trajectory design and generation. It is based upon a novel extension and investigation of new analytical and computational methods for solutions of Lagrange’s Implicit Function Theorem for trajectory design and optimization. Compared to current methods, this new approach not only offers the promise of improved computation time, but more importantly insight into higher-order sensitivities. The focus of this work will be on investigating ways to make this method practical for designing complex lunar descent and ascent trajectories, with an emphasis on lunar descent and braking. These applications are of direct interest to our nation’s current and future vision for space exploration and national defense.

    The Implicit function theorem originally due to Lagrange is an important result in analysis. Related methods for differentiation of implicit functions are even more important. The convergence of most versions of the Newton’s method to solve nonlinear equations and the theoretical foundations of virtually all algorithms for solving nonlinear differential equations (local existence and uniqueness of solutions) are closely related to these classical results. Other than serving as an important theoretical tool, the implicit function theorem and related concepts can be generalized to arrive at high order sensitivity equations for algebraic and differential equations about a given pre-computed solution. This perspective enables the conception of methods that construct families of neighboring solutions and blend them together in a fashion that simultaneously improves the accuracy of the local approximations and guarantees global piecewise continuity to a prescribed degree of partial differentiation.

    In the light of these theoretical and computational advances, it is felt that it is important to develop general methods for trajectory optimization, investigate the high order sensitivities, and in particular, develop methods that enable computation of extremal field maps for neighboring optimal trajectories. Relatively simple benchmark problems we have run show that computing families of neighboring optimal solutions that previously took several hours to generate can now be generated in a small fraction of one hour. This is especially important as NASA returns to the moon with the "anytime, anywhere" mindset. That is, NASA is designing vehicles to leave Earth for the moon at anytime and land at any desired location on the lunar surface. The same holds for mission aborts back to Earth. A quick, efficient, and effective analytical and computational tool is a top priority for NASA mission planners and trajectory designers. Improved optimal solutions will increase efficiency and reduce flight time and fuel costs, which, in turn, facilitate a more flexible mission with increased payload capacity. Ultimately, we seek to address time varying constraints along the trajectory.

    Working with me on this program is Graduate Research Assistant:

    • Matthew Harris, M.S. student
  9. Fault and Abort Tolerant Intelligent Ascent Control for Launch Vehicles

    Phase I: Vehicle modeling, simulation development, and preliminary control law synthesis

    NASA Johnson Space Center
    1 August 2008 - 31 July 2009
    Total award $129,569

    The next generation of vehicles that will take humans to the moon or Mars must be much more reliable and safer than both the manned (Space Shuttle Orbiter) and unmanned (e.g., Cassini, Mars Rover) vehicles that are currently being used. Fault tolerant control systems that autonomously adapt and safely and predictably recover from various equipment and system failures will be absolutely necessary. The Orion Crew Exploration Vehicle (CEV) program requires automated capability for numerous Guidance Navigation, and Control (GN&C) functions during the ascent phase of flight, particularly the automated execution of ascent abort scenarios. It is also a significant and challenging control problem because of elastic body modes, environmental uncertainties, possible control faults, and the need to be highly adaptable to possible mission aborts.

    This program seeks to conduct applied research to support current and future NASA goals in the area of launch vehicles (near term) and landers (intermediate term) by investigating and developing new approaches for the fault and abort tolerant ascent control of launch vehicles. The focus will be on novel and non-traditional control methods which have the potential to significantly improve current levels of safety in manned launch vehicles. Specifically, during the theory and algorithm development stage of this research, we will investigate ways to combine intelligent control techniques with adaptive control systems. This will enable the handling of time-varying parameters and environmental disturbances, while also providing a decision support function and the capability to learn and handle abort scenarios.

    Adaptive-Reinforcement Learning Control (A-RLC), an intelligent autonomous control methodology developed by the Vehicle Systems & Control Laboratory at A&M University and previously used for the control of aircraft and planetary entry vehicles, will be extended and tailored for the ascent phase of launch vehicles. A-RLC can make use of a variety of Machine Learning techniques, and determining the most efficient, implementable, and verifiable one will be a major task of the proposed work.

    One goal of the research will be to investigate and quantify the benefits / tradeoffs of using alternative and non-traditional approaches to fault tolerance and handling aborts, by determining the most efficient, implementable, and verifiable set from the following candidate list:

    • Intelligent Learning and Control
    • Intelligent Learning and Control
    • Machine Learning
    • Reinforcement Learning
    • Neural Networks
    • Fuzzy Logic
    • Advanced Learning / Function Approximation Techniques

    The approach taken for the proposed research will be to develop hierarchical, combined nonlinear Fault Tolerant Structured Adaptive Model Inversion Control (SAMI) with Adaptive – Reinforcement Learning Control (A-RLC), tailored specifically to launch vehicles. In this scheme, Fault Tolerant SAMI provides the fault tolerance capability. It is ideally suited to this application because of its flexibility for a variety of system types, and because a fault detection scheme is not required. The launch abort handling capability will be provided by A-RLC, which will learn how to safely and effectively handle non-nominal situations.

    Working with me on this program are Graduate Research Assistants:

    • Monika Marwaha, Ph.D. student
    • Amanda Lampton, Ph.D student
    • Anshu Narang, Ph.D student
  10. Machine Learning Control of Morphing Micro Air Vehicles

    Air Force Office of Scientific Research
    1 January 2008 - 30 November 2010
    Co-P.I. Dr. Suman Chakravorty
    Total award $450,000

    This project investigates a creative and bioinspired theory of learning control which is capable of addressing the essential functionalities of a morphing Micro Air Vehicle (MAV), and which is also extensible to capabilities such as flapping and perching. The objective is to address the optimal shape control of an entire air vehicle configuration as a function of flight condition, not just simple changes such as wing sweep angle or incidence angle. The project spans theory to computation to experiment, and incorporates machine learning concepts integrated with model reference adaptive control. It uses nonlinear synthesis and simulation models of appropriate fidelity validated and verified with a hardware testbed, and culminates in a flight test demonstration.

    Simulation File To Download

    The Defense Advanced Research Projects Agency (DARPA) defines a morphing air vehicle as a platform that is able to change its state substantially (on the order of 50%) to adapt to changing mission environments, thereby providing a superior system capability that is not possible without reconfiguration. In the context of intelligent systems, three essential functionalities of a practical morphing air vehicle are:

    1. When to reconfigure
    2. How to reconfigure
    3. Learning to reconfigure

    When to reconfigure is a major issue, as the ability for a given air vehicle to successfully perform multiple missions can directly be attributed to shape, at least if aerodynamic performance is the primary consideration. Each task or mission has an ideal or optimal vehicle shape, e.g. configuration. However, this optimality criteria may not be known over the entire flight envelope in actual practice, and the mission may be modified or completely changed during operation. How to reconfigure is a problem of sensing, actuation, and control. It is important and challenging since large shape changes produce time-varying vehicle properties, and especially, time-varying moments and products of inertia. The controller must therefore be sufficiently robust to handle these potentially wide variations. Learning to reconfigure is perhaps the most challenging of the three functionalities, and the one which has received the least attention. Even if optimal shapes are known, the actuation scheme(s) to produce them may be only poorly understood, or not understood at all; life long learning for reconfiguration strategies provide a robust evolutionary response to changing needs and missions. This permits the vehicle to be more survivable, and multi-role.

    Our approach combines Machine Learning and Adaptive Dynamic Inversion Control, and is called Adaptive-Reinforcement Learning Control (A-RLC). A-RLC is a control architecture and methodology for systems with a high degree of reconfigurability, such as changing shape during flight, flapping, perching, or morphing. The key difference between our approach and the very few existing approaches to morphing control lies with how learning is used. Morphing research reported in the current literature focuses on structures and actuation of at most three degrees of morphing freedom. For a morphing MAV, even if an optimal control law is known, the actuation scheme(s) to produce this capability may be only poorly understood, or not understood at all. A-RLC is capable of addressing the optimal shape control of an entire air vehicle configuration as a function of flight condition, not just simple changes such as wing sweep angle or incidence angle. A-RLC uses Structured Adaptive Model Inversion as the trajectory tracking controller for handling time-varying time varying inertias, large variations in aerodynamic and structural properties, parametric uncertainties, and disturbances. A-RLC uses Reinforcement Learning for learning the optimality relations between the operating conditions and the desired shape, over the lifespan of the vehicle. The Reinforcement Learning module has no prior knowledge of the relationship between commands and the dimensions of the vehicle, and it does not know the relationship between the flight conditions, costs and the optimal shapes. However, the Reinforcement Learning module does know the set of all possible inputs that can be applied. From complete ignorance of the system dynamics and actuation, A-RLC is capable of learning the optimal control policy (commands) which produce the optimal shape as a function of flight condition, while maintaining accurate flight path tracking. In addition, the Reinforcement Learning module of A-RLC can function in real-time, which results in robustness with respect to model errors and environmental disturbances during system operation. Our preliminary research has demonstrated that A-RLC works well for several nonlinear, time-varying, aerodynamically effected models. Key issues we will investigate are learning and control of the morphing, aeroelastic effects, hysteretic effects, and structural effects of the high fidelity, biologically inspired models developed in this research program.

    Working with me on this program are Graduate Research Assistants:

    and Undergraduate Research Assistants:

    • Brian Eisenbeis
    • Clark Moody
    • Claire Hazelbaker
  11. Prototyping Levels of Automation For Crew Exploration Vehicle (CEV)

    Rendezvous and Proximity Operations Tasks

    Collaborative Effort with GN&C Autonomous Flight Systems Office, NASA Johnson Space Center
    25 August 2005 - present
    Collaborator: Howard Hu

    A critical component in the CEV development is the delineation of decision-making authority between humans/computers (automation) and ground/onboard (autonomy). By finding the right balance of automation and autonomy, NASA can vastly improve the probability of mission success, increase safety, and decrease overall cost. To identify the appropriate levels of automation and autonomy to design into a human space flight vehicle NASA has created a method called the Function-specific Level of Autonomy and Automation Tool (FLOAAT).

    Function-specific Level of Autonomy and Automation Tool Scales

    The purpose of this research is to prototype a sub-set of the Rendezvous and Prox Ops functions at the levels of automation specified using FLOAAT. By prototyping at these levels the accuracy of the FLOAAT outputs can be judged. The research will also be used to apply modern decision-making algorithms to help improve the efficiency, safety, and quality in the execution of selected Rendezvous and Prox Ops planning tasks. This research will deal only with the breakdown of human versus computer responsibility (automation) and therefore will not address the issue of ground versus on-board responsibility (autonomy). The issue of autonomy, although important, is difficult to prototype until a more detailed design of the ground control architecture and on-board computing and display capabilities is conducted.

    Function Level Ti Ellipse

    Specific tasks and research objectives:

    • Prototype selected Rendezvous and/or Prox Ops functions at the levels of automation determined by the Function-specific Level of Autonomy and Automation Tool (FLOAAT) process
    • Evaluate the prototype versions by comparing to Shuttle/ISS implementations of the same functions
    • Use this comparison to evaluate the quality of the FLOAAT recommended level of automation
    • Evaluate the selected decision-making algorithm

    A final evaluation will be made to determine if the level of automation was appropriate for each prototyped function and provide suggestions for improvement. This includes an evaluation of the prototyping process, AI techniques used, and the effectiveness of operating at the levels of automation specified by the FLOAAT process. The successfulness of the prototyping effort will be used to gauge the accuracy of the FLOAAT tool to select appropriate levels of automation. It will also determine the applicability of the selected decision-making algorithms for use in human spaceflight.

    Working with me on this program is Graduate Research Assistant:

    • Jeremy Hart
  12. UAV Hingeless Flight Controls via Active Flow Control, Phase II

    Aeroprobe Corporation and Air Force Research Laboratory
    1 April 2006 - 28 March 2008
    Total award $375,000

    Flow control seeks to modify the flow so that it behaves in a different (favorable) fashion compared to no control. It may be used to control or promote boundary layer transition, limit flow separation, replace conventional Aerodynamic Control Effectors (ACE) providing significant stealth benefits, augment lift, modify acoustic emissions or reduce drag. The potential benefits of flow control are many and varied: reduced structural weight, greater resistance to battle damage and improved survivability (fewer components and linkages), improved performance (drag relates to the number of breaks in the aircraft's external surface), greater flight envelopes (separation suppression), reduced operational cost (fewer components) and greater stealth capability.

    Flow control may be implemented passively or actively. Active flow control is seen as a means to performance enhancement and a way to replace conventional ACE. Active methods for flow control include blowing, suction, moving surface elements, oscillatory blowing/suction, wall oscillation, vibrating ribbons, and zero-mass-flux, finite momentum actuators or Synthetic Jet Actuators (SJAs).

    This research seeks to answer specific questions about active flow control:

    1. What characteristics are necessary for a flow control effecter to be functional in this application?
    2. What vehicle configuration would benefit the most from such an effecter?
    3. How can the effects of the flow control be modeled?
    4. What type of flight control laws and feedback mechanisms would be necessary to control the aircraft via flow control actuators?
    5. How would using non-conventional flow effecters improve aerodynamic performance?

    In this research, we develop and implement active flow control in an unmanned aerial vehicle (UAV) configuration to show how the design and application of active fluidic control may be used to improve the performance of a proposed UAV. The fluidic control is implemented using a combination of SJA and trailing edge continuous blowing (or SJA's). The flow control may be used to extend the angle-of-attack envelope by suppressing flow separation and to achieve hingeless control by modifying the wing's circulation through trailing edge flow manipulation (using a modular jet flap or circulation control). At high incidence, upper surface flow control using SJA's is used to re-attach the flow while trailing edge blowing is used to achieve control authority. Our SJA design is well validated and has been shown to be reliable and effective in many investigations. Proposed methods for achieving aerodynamic modeling, sensors for feedback to aid in control, as well as control law are investigated.

    Specific tasks and research objectives:

    • Determine a suitable actuator and implementation for flow control.
    • Unmanned aerial vehicle conceptual configuration layout.
    • Feedback methodology and implementation.
    • Modeling of effects of proposed ACE.
    • Control law design.
    • Performance improvement estimates.
    • Demonstration of key technology: ACE effectiveness.

    Working with me on this program is Graduate Research Assistant:

    • Tom Wagner
  13. Fault Tolerant Nonlinear Adaptive Control for Mars Atmospheric Flight, Phase I:

    Vehicle modeling, simulation development, and preliminary control law synthesis

    GN&C Design and Analysis Branch, NASA Johnson Space Center
    1 January - 31 May 2006
    Total award $20,840

    The next generation of vehicles that will take humans to the moon or Mars must be much more reliable and safer than both the manned (Space Shuttle Orbiter) and unmanned (e.g, Cassini, Mars Rover) vehicles that are currently being used. Fault tolerant control systems that autonomously adapt and safely and predictably recover from various equipment and system failures will be absolutely necessary. Because the science missions will be more demanding, and the planetary operating environments more extreme and largely unknown in composition and terrain, this newer generation of vehicles will need advanced control systems capable of handling large environmental uncertainties. For example, a vehicle that must land on Mars needs a control system that can cope with uncertainties in atmospheric parameters, such as density and pressure. Additionally, the Mars terrain is composed of different types of soil and rocks which will make landing very difficult. Several hazard avoidance systems are being researched now, and it is very important to have a control system that can be integrated with such algorithms so that it can adapt its parameters to maintain the system stable at all times. One of the techniques currently used to design controllers for nonlinear time-varying systems, such as the one for a Mars Lander is traditional gain-scheduling. This method requires extensive modeling, design, and analysis since the designer picks a finite number of points and designs a different control law for each of these operating conditions. An example of this is the flight control system of the Space Shuttle Orbiter. During the vehicle's reentry phase, the control system dictates whether to use reaction control system (RCS) jets or aerodynamic control surfaces to generate the necessary torque to follow the given trajectory. When the vehicle is flying at high altitudes, the atmosphere is very thin and the aerodynamic surfaces are not effective; when the vehicle is lower in the atmosphere, the aerodynamic surfaces are very effective and there is no need to consume more fuel by firing the RCS jets. However, this approach could not be used in a Mars Lander entry vehicle because it requires very accurate atmospheric models and vehicle models.

    The broad objective of this research is to conduct the theory-computation-experiment cycle for a Mars Lander adaptive control system to support the design of advanced missions and systems for the human exploration of space. Specifically, during the theory and algorithm development stage of this research, we will investigate ways to apply intelligent control techniques such as neural networks and reinforcement learning to adaptive control systems. This will enable the handling of time-varying parameters and environmental disturbances, while also being applicable to the control of nonlinear systems. It is important to validate and test out theory using both numerical simulation and hardware. Work performed during Summer 2006 as part of a Summer Graduate Internship at NASA Johnson Space Center will use the planetary landers simulation and hardware demonstrator systems to test out the advanced adaptive controllers.

    Specific tasks and research objectives:

    • Develop Linear and Nonlinear Vehicle Models
    • Develop Matlab/Simulink Simulation
    • Define and Characterize Atmospheric Uncertainties
    • Synthesize Baseline Adaptive Controller
    • Documentation of Results

    Working with me on this program is Graduate Research Assistant:

    • Carolina Restrepo
  14. Adaptive Flight Controller, Phase I

    Brand X Aerospace
    15 December 2005 - 31 January 2006
    Total award $20,494

    Development of nonlinear Structured Adaptive Model Inversion (SAMI) control laws for aircraft.

  15. Model Estimation for Adaptive Trajectory Reshaping and Control

    Knowledge Based Systems Incorporated through Air Force Research Laboratory
    1 November 2005 - 15 March 2007
    Total award $140,108

    To increase the autonomy of Unmanned Aerial Vehicles (UAVs), a desired feature is to have onboard intelligence for making decisions and planning maneuvers. To define and execute a maneuver we need an efficient means to generate a reference trajectory onboard and in near real-time. Also, this generated reference trajectory has to be consistent with the available control power and the airframe capability. To generate a feasible reference trajectory, accurate knowledge of the plant model is necessary, however; an accurate plant model is not always available and the plant model may also change due to control failures or damage to the UAV.

    This research will develop a model estimation technique that can generate a plant model which is accurate enough to be used for generating feasible reference trajectories.

    The central idea is to use estimates of the plant model based on geometric information of the aircraft and update this estimate based on the estimate obtained from real-time system identification of the plant from the input-output signals. Suppose there exists a visual sensor system that can identify the geometry of the aircraft on the fly. This information can be used to estimate the stability and control characteristics of the aircraft using methods contained in the USAF Stability and Control Datcompendium (DATCOM). These methods have been traditionally used in preliminary calculations for aircraft design. For the current research they can provide the approximate plant model for the entire flight envelope based only on the geometry of the aircraft. This approximate plant model can serve as a starting point for the model estimation based on the input-output data of the system. Thus, the estimation algorithm is a synergy of Datcom and model estimation from measurements.

    Specific tasks and research objectives:

    • Identify and formulate plant estimation model
    • Generate DATCOM estimates
    • Formulate fusion model
    • Ensure stability and convergence of the estimated model
    • Evaluate model using Matlab/Simulink based numerical simulation
    • Evaluate KBSI Adaptive Trajectory Tracking Control
    • Validation and verification using real-time simulation

    Working with me on this program is Graduate Research Assistant:

    • Post-Doctoral Researcher Dr. Puneet Singla
    • Theresa Spaeth
  16. OASIS: A System for Pinpoint Landing and Hazard Avoidance On Lunar and Martian Surfaces for both Manned and Unmanned Landers

    Collaborative Effort with NASA Langley Research Center
    15 May 2005 - present
    Collaborator: David L. Raney

    Supported by:
    Texas Institute of Intelligent Bio-Nano Materials and Structures for Aerospace Vehicles (TiiMS) and NASA Langley LAARS Program

    The NASA Exploration Initiative Spiral 2 exploration mission architecture involves heavy reliance on both manned and unmanned landers to pre-position and then leverage assets on the lunar surface.

    This research is seeks to develop an Optionally Autonomous Surface Intelligence System (OASIS) to provide a pinpoint landing and hazard avoidance capability for both manned and unmanned landers that could operate on either the lunar or martian surface, to support Spiral 2 and follow-on Exploration Mission activities. The OASIS Project would develop a dynamic cost map-based guidance and control system along with a variable-autonomy interface for optional pilot control of descent and landing. These systems would be integrated with a real-time terrain mapping/hazard detection sensor to generate surface feature data for the formulation of the guidance cost map.

    Crossrange and downrange maneuvering during the terminal landing phase is generally expensive in terms of propellant budget. To minimize the need for corrections during the final landing phase, the OASIS project will develop active control for early phases of reentry where minor guidance corrections and energy management maneuvers are highly leveraged. Additionally, the potential to actively control the descent path while the vehicle is on the chute will be investigated for Mars landers. Although steerable parafoil chute systems have been demonstrated on earth, such systems will pose novel dynamics and control challenges when scaled appropriately for the martian atmosphere. Finally, the OASIS Project will include a capability for real-time precision resolution of lunar/martian surface features to enable high precision terrain-based navigation to the final landing site during the terminal flight phase.

    Variable autonomy will be a major research focus since manned landers will require the ability for human monitoring and optional intervention via an operator interface that is both intuitive and flexible. The OASIS Project will develop and implement a human-machine interface system that relates terrain hazard information and precision guidance through tactile, auditory, and visual cues, enabling the human to continuously elect a level of interaction that ranges from pure oversight to full manual control.

    The high value of assets on board the lander will necessitate active detection and avoidance of surface hazards including large rocks, crevasses, and excessively rough or inclined terrain. The OASIS Project will develop a hazard-avoidance cost mapping algorithm that provides information to the lander's terrain-based guidance and navigation system. This function will utilize data from the high precision surface feature resolution sensor suite.

    Unprecedented landing precision will be required to complete the Spiral 2 activity. Historically, the touchdown precision of current entry, descent and landing systems ranges from approximately 3 km at best to tens of km in some cases. By contrast, the required precision for missions that leverage pre-positioned assets will be on the order of tens of meters. The OASIS Project will develop a system to provide active control through all phases of entry, descent, and landing to enable the required degree of landing precision.

    Specific tasks and research objectives:

    • Acquire, modify, and host representative simulation model of manned lander.
    • Acquire and implement relevant atmosphere and terrain models for lunar and martian landing scenarios.
    • Develop and implement algorithms for fault tolerant control during early entry phases to enable high precision touchdown site acquisition.
    • Investigate potential for control on chute to reduce propellant requirements for high precision Mars landing.
    • Develop and implement active hazard detection sensor models and lander guidance cost mapping algorithms
    • Develop hazard avoidance and precision landing cueing interface for piloted lander simulation.
    • Integrate cost mapping guidance with piloted lander cueing interface.

    Working with me on this program is Graduate Research Assistant:

    • Theresa Spaeth
  17. UAV Hingeless Flight Controls via Active Flow Control, Phase I

    Aeroprobe Corporation
    1 May 2005 - 31 January 2006
    Total award $33,000

    Flow control seeks to modify the flow so that it behaves in a different (favorable) fashion compared to no control. It may be used to control or promote boundary layer transition, limit flow separation, replace conventional Aerodynamic Control Effectors (ACE) providing significant stealth benefits, augment lift, modify acoustic emissions or reduce drag. The potential benefits of flow control are many and varied: reduced structural weight, greater resistance to battle damage and improved survivability (fewer components and linkages), improved performance (drag relates to the number of breaks in the aircraft's external surface), greater flight envelopes (separation suppression), reduced operational cost (fewer components) and greater stealth capability.

    Flow control may be implemented passively or actively. Active flow control is seen as a means to performance enhancement and a way to replace conventional ACE. Active methods for flow control include blowing, suction, moving surface elements, oscillatory blowing/suction, wall oscillation, vibrating ribbons, and zero-mass-flux, finite momentum actuators or Synthetic Jet Actuators (SJAs).

    This research seeks to answer specific questions about active flow control:

    1. What characteristics are necessary for a flow control effecter to be functional in this application?
    2. What vehicle configuration would benefit the most from such an effecter?
    3. How can the effects of the flow control be modeled?
    4. What type of flight control laws and feedback mechanisms would be necessary to control the aircraft via flow control actuators?
    5. How would using non-conventional flow effecters improve aerodynamic performance?

    In this research, we develop and implement active flow control in an unmanned aerial vehicle (UAV) configuration to show how the design and application of active fluidic control may be used to improve the performance of a proposed UAV. The fluidic control is implemented using a combination of SJA and trailing edge continuous blowing (or SJA's). The flow control may be used to extend the angle-of-attack envelope by suppressing flow separation and to achieve hingeless control by modifying the wing's circulation through trailing edge flow manipulation (using a modular jet flap or circulation control). At high incidence, upper surface flow control using SJA's is used to re-attach the flow while trailing edge blowing is used to achieve control authority. Our SJA design is well validated and has been shown to be reliable and effective in many investigations. Proposed methods for achieving aerodynamic modeling, sensors for feedback to aid in control, as well as control law are investigated.

    Specific tasks and research objectives:

    • Determine a suitable actuator and implementation for flow control.
    • Unmanned aerial vehicle conceptual configuration layout.
    • Feedback methodology and implementation.
    • Modeling of effects of proposed ACE.
    • Control law design.
    • Performance improvement estimates.
    • Demonstration of key technology: ACE effectiveness.

    Working with me on this program is Graduate Research Assistant:

    • Monish D. Tandale
  18. Control of Forward Reaction Control System (RCS) Jets for Atmospheric Flight Risk-Reduction of Shuttle Orbiter During Entry

    Collaborative Effort with
    Peter F. Covell, NASA Langley Research Center
    Alan Strahan, NASA Johnson Space Center

    1 January 2005 - 31 December 2006
    Supported by:
    NASA Johnson Undergraduate Cooperative Student Program
    NASA Johnson Graduate Internship Program
    Texas A&M University Flight Simulation Laboratory

    During shuttle orbiter entry, failure or degradation of flight control effectiveness or vehicle aerodynamics may result in loss of vehicle control. The loss of the shuttle orbiter Columbia was an example of this, and other failure scenarios include:

    1. Debris impact renders aft Orbital Maneuvering System (OMS) / Reaction Control System (RCS) inoperable, or degrades aero surface control effectiveness.
    2. Wing damage induces an aerodynamic asymmetry greater than the baseline flight control system can handle.
    3. Vehicle damage requires flight at off-nominal attitudes to protect damaged regions from extreme thermal load.

    In theory, the forward RCS jets can be used to provide additional torque to maintain yaw control in situations where the aft RCS jets alone are insufficient. The forward RCS jets are not currently used for atmospheric flight control on the shuttle orbiter because the baseline controller was designed to sufficiently handle the present flight/risk envelope without using them. This was largely due to an old aerodynamics "myth" that said the forward RCS jet interactions can be adverse, and thus unsuitable for vehicle control purposes. Although it is true that adverse effects can occur at low angle-of-attack, they are far less likely to occur at higher angle-of-attack, and in August 2004 an aerodynamics Proposal Review Team re-visited the concept and concluded that there were no major aero-mechanic issues that would prohibit use of forward RCS for entry control.

    The objective of this research is to design a controller that uses the forward RCS jets to provide additional torque for a damaged vehicle, or a vehicle with damaged control surfaces, or damaged aft jets, to augment the nominal controls during entry. Although the shuttle orbiter retains a fuel reserve of forward RCS jet propellant during entry, the ratio of forward and aft jet activity must be balanced to stay within availability constraints and center of gravity limitations. Use of the aileron and rudder trim limits can help this. A control allocation scheme couple with a fault tolerant Structured Adaptive Model Inverse (SAMI) adaptive controller will be used to detect damage induced torque effects and failed jets.

    Specific tasks and research objectives:

    • Assess value of using forward RCS during entry failure scenarios.
    • Develop updated aero model by extending current aero database above Mach 4.5 to characterize RCS jet interactions.
    • Design wrap-on control law to include forward RCS.
    • Conduct non real-time simulator evaluation.
    • Conduct preliminary risk assessment.
    • Conduct real-time simulator evaluation using Shuttle Engineering Simulator (SES).
    • Implement control allocation scheme.
    • Synthesize and develop Structured Adaptive Model Inverse (SAMI) adaptive controller.

    Potential long-term study elements include investigation of the multi-axis RCS contributions afforded by the forward pitch RCS jets, and the aft pitch and roll jets.

    Working with me on this program is Undergraduate Research Assistant:

    • Carolina Restrepo
  19. Support for Autonomous Aerial Refueling System (AARS) Demonstration

    Star Vision Technologies
    1 April 2005 - 31 October 2005
    Co-P.I.David W. Lund
    Total award $30,000

    This program is an experimental feasibility assessment of an innovative and robust Autonomous Aerial Refueling System (AARS). The system has been tailored to specific powered munitions and will provide significant battlefield enhancements by allowing persistent and sustained air operations. The AARS proposed is the result of several years of studies and feasibility assessments and includes a novel vision-based relative navigation sensor, an Intelligent Supervisory Control system and customized refueling hardware. Leveraging prior feasibility assessments, the proposed Phase I effort will include an innovative flight experiment including the critical components of the AARS, a Boeing Phantom Works donated vehicle, and ground support contributions from the Texas A&M University's Flight Mechanics Lab. The proposed Phase I flight experiment will help benchmark a high fidelity simulation model of the AARS with a powered munition. The flight experiment will address key feasibility issues and mitigate risks of conducting a Phase II aircraft-to-aircraft demonstration of the AARS in a relevant environment.

    Successful unmanned refueling operations require a control system to govern the receiver approach, fuel system prep, stand-off, proximity engagement and hook-up. There are also aborts, emergency separations and fuel system shutdown commands that will have to occur under the direction of the overall AARS supervisory controller. Texas A&M University has developed a unique intelligent supervisory control system for automated rendezvous and docking that is being licensed to StarVision Technologies and Sargent Fletcher Inc. The intelligent supervisory control system leverages decades of manned refueling experience from Sargent Fletcher in a rules-based finite state logic machine. An appropriate communication system is used to transfer navigation information from tanker to receiver. The intelligent supervisor resides as code within the tanker and receiver flight controllers.

    For refueling powered munitions with strict volume and mass constraints a new type of fuel delivery, vehicle mating, and receiver probe mechanisms were required. Sargent Fletcher has developed a set of customized and unique refueling hardware assemblies that include the triangle boom, the flycatcher, and the microprobe.

    Our team has devised an innovative technique to validate the feasibility of the AARS in a relevant flight environment by leveraging significant hardware and facility contributions from the team members. The experiment will include a receiver vehicle flying in proximity to a moving target mounted from a truck. The experiment will be conducted at the Texas A&M University Flight Mechanics Lab (FML). The receiver air vehicle will pursue and dock with a target mounted on a moving truck. A mast is mounted to the truck to locate the VisNav beacons above the wake of the moving truck. A cage is provided to allow for radio control (RC) piloting of the receiver vehicle for aborts and emergencies. This also allows the RC pilots to be within visual range of the receiver throughout the proximity operation.

    This low-cost and innovative flight experiment approach coupled with high fidelity simulations will allow the AARS to advance to a higher technology readiness level and proceed along the roadmap to an aircraft to aircraft docking demonstration. The data obtained from this experiment will allow the AARS team to modify the high fidelity Maltab/Simulink models with actual flight data and use the simulations to then test the AARS in a greater set of possible mission scenarios.

    Specific tasks and research objectives:

    • Develop overall system architecture and coordinate the requirements and interfaces of the AARS. The main product is a coordinated set of tasks that efficiently produce the desired combination of simulation and flight experimentation.
    • Tailor the Intelligent Supervisory Control architecture to the specific application of the AARS. The output of this task is the software code that is compatible with the demonstration vehicle and a potential tanker vehicle (for Phase I this is a moving truck).
    • Develop a high fidelity simulation of the planned flight experiment that can be calibrated with actual flight data. This task will result in MATLAB/SIMULINK based simulation of the receiver vehicle and a model of the moving truck. This task includes development of models, controllers and anticipated flight trajectories, and evaluation of the simulated versus actual data.
    • Prepare the ground vehicle and support equipment for the flight experiment.
    • Integration of the AARS components into the receiver flight vehicle and conduct the flight experiments.

    Working with me on this program are Graduate Research Assistants:

    • Jeff Morris
    • Tom Wagner
    • James Doebbler
  20. Prediction of Icing Effects on the Stability and Control of Light Airplanes

    Aeronautical and Educational Services Company
    15 March 2005 - 30 June 2005
    Total award $18,884

    The accumulation of ice on aircraft in flight is one of the leading causes of general aviation accidents, and to date only relatively sophisticated methods based on detailed empirical data and flight data exist for its analysis. A useful tool for a basic analysis of icing effects on airplane performance, stability, and control is an accurate yet simplified dynamical simulation model, based upon relatively simple data for airplane configuration, propulsion system, mass properties, and icing data.

    This research develops such a tool, and applies it to the investigation of stability and control characteristics, and climb and descent performance of a representative light aircraft in icing conditions. Empirical data and DATCOM methods will be used to develop a linear time-invariant, six degree-of-freedom state-space model of a Cessna 208. Validation of the model will be accomplished by comparison to commercially available flight test data for a Cessna 208. To investigate the effect of ice accretion on stability and control characteristics, climb maneuvers, and descent maneuvers, existing icing data for a light aircraft of similar configuration was incorporated into the model. It is assumed here that the icing accretion is fully developed, and configurations of wing icing alone; horizontal tail icing alone; and combined wing and horizontal tail icing will be analyzed using the component build-up method. A vortex lattice computer code will also be used to validate the results.

    Specific tasks and research objectives:

    • Generate state-space linear airframe models of a representative light airplane in the clean configuration. Perform climb comparisons to commercially obtained flight data for the same aircraft to validate the models.
    • Incorporate icing effects on the state-space linear clean airframe models. Verify icing effects with climb comparisons to published data for longitudinal dynamics.
    • Refine icing models with a Computational Fluid Dynamics (CFD) code, and incorporate asymmetries stemming from icing buildup on wing and horizontal tail.
    • Using the simulation codes developed, evaluate a minimum of 20 test case scenarios.

    Working with me on this program is Graduate Research Assistant:

    • Amanda Lampton
  21. Research Experiences for Undergraduates: Nanotechnology and Materials Systems

    National Science Foundation
    1 March 2005 - 28 February 2008
    Co-P.I.s Dan Davis, Dimitris C. Lagoudas, John L. Junkins, Othon K. Rediniotis, John D. Whitcomb, and James Boyd
    Total award $250,000

    This Research Experience for Undergraduates (REU) program on Nanotechnology and Materials Systems supports 12 engineering and science students each year for three years in a 10-week summer research experience at the Texas A&M University. It offers projects with foci on nanoscience and nanotechnology, materials science, and engineering systems. Projects are selected to span the physical scales from nano through macroscopic systems. The primary goal is to present a model program for increasing the number of U.S. science and engineering students entering graduate studies and pursuing research and academic careers. This goal is pursued through four (4) coordinated components: 1) A challenging research experience in exciting science and engineering fields of nanotechnology and materials; 2) A close and personal mentoring relationships with by senior faculty and researchers, administrators, graduate student role models and peer groups of other undergraduate students; 3) Exposure to the research communities at regional universities, industries and government agencies involved in nanotechnology and materials research to further foster interest in research careers and graduate studies; and 4) Information on graduate school including seminars on GRE preparation, application procedures, and funding a graduate education. Additionally, the program sponsors weekly educational field trips to industrial and governmental agencies such as NASA Johnson Space Center (Houston, TX), Lockheed-Martin Corporation (Fort Worth, TX) and Zyvex Corporation (Richardson, TX). These field trips provide some real-world context to the broad multidisciplinary nanotechnology and materials research the REU students experience in the laboratory. Also, the REU-Site students participate in an annual regional research conference on the multidisciplinary areas of functionalized nanomaterials, multifunctional materials systems, biomaterials and devices, multiscale modeling, novel design concepts, and intelligent systems.

    2005 Topic: Space-Based Antenna Morphing using Adaptive-Reinforcement Learning Control

    The state of the art in spacecraft communication requires that multiple antennas be mounted on a single spacecraft so as to permit communication with multiple ground stations, many of which have unique receivers and transmitter characteristics. One approach currently being investigated is to use a reconfigurable constellation of satellite antennas, in which each antenna is a single satelite. Another approach is to use a single antenna capable of altering its geometry to achieve world-wide compatibility between receivers and transmitters. The implication of a single space antenna capable of altering its geometry is a significant capability for spacecraft.

    This research seeks to develop and demonstrate the feasibility of a reconfigurable antenna shape controller that can achieve and control the optimal antenna shape, on demand. Shape-Memory Alloys (SMA) have been employed to enhance structural properties and increase the ability of structures to adapt and conform as desired, and antenna elements rigged with SMA actuators will be used here as the actuation element. A morphing control aproach called Adaptive-Reinforcement Learning Control will be used to efficiently alter the antenna shape to achieve optimal concavity. This controller is capable of independently learning the optimal concavity in a lifelong sense, thus allowing a space-based radar and communication systems to decrease the quantity of antennas currently mounted on spacecraft.

    Specific tasks and research objectives:

    • Generate original Reinforcement Learning algorithm.
    • Construct a simple finite element model of a parabolic antenna element.
    • Quantify input/output behavior of a space antenna utilizing SMA actuators.
    • Demonstrate reconfiguration capability using simulation.

    Working with me on this program is Undergraduate Research Assistant:

    • Holly Feldman
  22. Flight and Sensor Simulation for Autonomous Aerial Refueling Technology Development: Phase II

    Star Vision Technologies
    1 January 2005 - 15 September 2005
    Total award $25,000

    The Autonomous Aerial Refueling (AAR) Flight Demonstration, a joint effort between Sargent Fletcher Incorporated, StarVision Technologies, and Texas A&M University is, to the best of our knowledge, the first time two remotely piloted vehicles will attempt to hook-up in a simulated refueling configuration. It is a critical step toward demonstrating that in-flight refueling is a feasible way to extend the range and endurance of Class III vehicles.

    Specific tasks and research objectives:

    • Demonstrate both ground-to-ground and air-to-ground autonomous docking maneuvers.
    • Preparare for an air-to-air (Phase III) demonstration.
    • Define the allowable approach/alignment envelope required for successful engagement of the microprobe and flycatcher basket.
    • Conduct simulations to evaluate the various receiver to tanker approach options, including VisNav sensor field of view limitations and receiver controllability limitations when evaluating approach trajectories.
    • Assess autopilot modes and modifications through definition of autonomous aerial refueling operation modes.
    • Simulate and analyze the effects of atmospheric and refueler-induced turbulence on the refueling operation.
    • Support assessment of the rendezvous capability and accuracy of refueler and receiver, and make recommendations to implement a successful refueling rendezvous.

    Working with me on this program are Graduate Research Assistants:

    • Changwha Cho
    • Roshawn Bowers
    • Tom Wagner
  23. High Fidelity Flight Simulation of Autonomous Air Refueling Using a Vision Based Sensor

    Star Vision Technologies
    1 July 2004 - 31 December 2004
    Total award $38,000

    A high fidelity Autonomous Air Refueling (AAR) simulation will mitigate the risk associated with these demonstrations, particularly the air-to-ground and air-to-air flight tests. While simulations of individual components, such as the VisNav sensor, have already been implemented, no comprehensive simulation has been created that realistically captures the behavior of the combined AAR system (sensors, controller, tanker, and receiver). Such a simulation will allow for the identification and resolution of system deficiencies before flight testing, where unforeseen problems are more costly in terms of schedule and budget.

    The simulation will be able to test scenarios involving various tanker and receiver vehicle relative range and velocities, various lighting conditions, and disturbance effects. It will be used to:

    1. Validate the adequacy of the VisNav navigation solution
    2. Validate the proposed controller in terms of performance and robustness
    3. Generate a set of operating conditions where the system is expected to perform.

    Specific tasks and research objectives:

    • Creat simulation master plan, which will define the overall architecture and internal structure of the simulation, as well as the function and interfaces of each component. It will specify the programming language (Matlab, Simulink, C, etc.) and programming style, define global variables, and outline the required documentation for the each piece of the simulation.
    • Develop high fidelity model of the VisNav system, which will be used to validate the adequacy of the VisNav navigation solution for AAR. This model will include realistic sensor noise, field of view considerations, and provisions for various lighting conditions. The model will be validated using experimental data and/or existing VisNav simulations.
    • The linear state-space model of the Maxdrone obtained in Phase 0 will be modified to include the effects of the triangle boom assembly. The flying qualities of the tanker and boom assembly will be analyzed. If necessary, an autopilot that will allow the tanker to maintain steady, level trim during the docking maneuver will be designed. The tanker model and autopilot will then be incorporated into the simulation as specified by the master plan.
    • Design a feedback controller to maintain stability during flight. The receiver model and autopilot will then be incorporated into the simulation as specified by the master plan.
    • Evaluate options for integrating the VisNav sensor into the Air Dominator vehicle and conduct simulation and analysis for each configuration option.
    • Test and Evaluate Simulation.
    • Document results.

    Working with me on this program are Graduate Research Assistants:

    • Changwha Cho
    • Roshawn Bowers
    • Jeff Morris
    • Tom Wagner
  24. Autonomous Intelligent Agents and Displays for Automation and Real-Time Simulation of Non-Controlled Airports

    NASA Langley Research Center through Research Triangle Institute
    1 March 2004 - 31 December 2004
    Total award $152,754

    The existing air transport system in US cannot meet the public demand for safety, higher-speed mobility, and increased accessibility. It mostly results from the dominant hub-and-spoke model that results in a concentration of a large percentage of the air traffic at a few hub airports. Meanwhile, there are about 5400 existing public-use-landing facilities around the country in the current National Airspace System (NAS), but scheduled air carriers serve only about 660 of these facilities. Revolutionary technologies are in great need to enhance the transportation capabilities of the nation's small aircraft transportation network, and thus relieve the congestion of the hub airports.

    The ongoing research of terminal airspace management around non-radar, non-tower general aviation airport is both from the point of views of ground system and airborne system. First, an automated ground arrival/departure system is proposed for this kind of small non-controlled airports. Functional description of the airport terminal area infrastructure and automated terminal operations and procedures are defined first, then several types of intelligent agents with negotiation functions are developed in the automation system. Second, an aircraft approaching and landing assistant (AALA), an advanced airborne cockpit system, is proposed aiming at automating part of the pilot decision-making process and thus to decrease the pilot workload and improve flight safety. This research is an extension of previous ten years' research in intelligent cockpit computing in FSL. Finally, a distributed air/ground ATM system is proposed to realize the objectives of accommodating higher volume traffic, increasing flight safety and efficiency at small non-controlled airports. In this system, pilots, with the aid of advanced cockpit systems and automated ground controllers, assume the primary responsibility in assuring the airspace safety.

    This research aims to design an automated arrival/departure system for non-controlled airports and thus meet the needs described above. Functional description of the airport terminal area infrastructure and automated terminal operations and procedures are defined first, then several types of intelligent agents with negotiation functions are developed in the automation system. Moreover, an approaching and landing assistant, which is an advanced cockpit system, is incorporated aiming at automating part of the pilot decision-making process and thus to decrease the pilot workload and improve flight safety. Finally, simulation methodology is determined with a full description of hardware and software used by the simulation.

    A high fidelity simulation system is of great importance in design, development and evaluation phases of a new system. Air-traffic Information Management System (AIMS), a program currently under construction, aims at providing fast time simulation for evaluating the capacity, efficiency, and safety of the proposed distributed air/ground ATM system. Moreover, when connected to the EFS, it is able to provide real time simulation for human factor evaluation of cockpit system design.

    Specific tasks and research objectives:

    • Investigate functionality and performance of an automated arrival/departure system, addressing the high traffic volume problem in non-controlled airports.
    • Define the multi-layer air traffic space around the terminal area of non-controlled airports, and develop the communication and negotiation procedures necessary for managing the traffic flow.
    • Provide improved terminal area arrival flow planning algorithms, including arrival sequencing and arrival flow re-planning, given a perturbation such as runway change or severe weather.
    • Develop a traffic scenario generator, which provides great flexibility of choosing initial weather conditions, topological data, traffic situation, flight plan for each aircraft, flight procedures, and ATC rules.
    • Develop an intelligent cockpit system, a pilot decision aid tool that assists pilots in decision-making during the high workload flight phase in a complex environment.

    Working with me on this program are Graduate Research Assistants:

    • Jie Rong
    • Yuanyuan Ding
    • James Doebbler
    • Paul Gesting
    • Tom Wagner
    • Steve Wollkind

    and Undergraduate Research Assistant:

    • Klye Helbing
  25. Autonomous Air Refueling Concepts For Area Dominator Vehicle

    Boeing Phantom Works Through Sargent Fletcher, Inc. and StarVision Technologies
    1 November 2003 - 30 October 2007
    Total Award $358,000

    A high fidelity AAR simulation will mitigate the risk associated with these demonstrations, particularly the air-to-ground and air-to-air flight tests. While simulations of individual components, such as the VisNav sensor, have already been implemented, no comprehensive simulation has been created that realistically captures the behavior of the combined AAR system (sensors, controller, tanker, and receiver). Such a simulation will allow for the identification and resolution of system deficiencies before flight testing, where unforeseen problems are more costly in terms of schedule and budget.

    The simulation will be used to 1) validate the adequacy of the VisNav navigation solution, 2) validate the proposed controller in terms of performance and robustness, and 3) generate a set of operating conditions where the system is expected to perform. The simulation will be able to test scenarios involving various tanker and receiver vehicle relative range and velocities, various lighting conditions, and disturbance effects.

    Working with me on this program are Graduate Research Assistants:

    • Roshawn E. Bowers
    • Changwha Cho
    • Jeff Morris
    • Tom Wagner
  26. Autonomous Aerial Refueling Demonstration, Phase I

    Air Force Research Laboratory Munitions Directorate Through Sargent Fletcher, Inc.
    1 September 2003 - 30 June 2004
    Co-P.I.s John L. Junkins, Donald T. Ward, and David W. Lund
    Total Award $100,000

    This project will flight test the first closed-loop hook-up of two Unmanned Air Vehicles (UAVs) in a simulated air-to-air refueling configuration. It is the critical step toward a practical and routine Autonomous Air Refuleing (AAR) capability to extend the range and endurance of Class III air vehicles (between 5 and 200 pounds). This project will also further develop the VisNav vision based relative navigation system, and synthesize control laws to enable accurate AAR. Since only slight modifications to legacy refueling systems are required, this technique has the potential to minimize costs required to upgrade manned refueling assets to autonomous refueling. Four phases are planned. The objectives of Phase I include demonstrations of both ground-to-ground and air-to-ground autonomous docking maneuvers, and development leading to the air-to-air flight demonstration of Phase II.

    A high-fidelity simulation will be created, consisting of the VisNav sensor, the tanker air vehicle, and the receiver air vehicle. This high fidelity model of the VisNav system will be used to validate the adequacy of the VisNav navigation solution for AAR. The VisNav model will include realistic sensor noise, field of view considerations, and provisions for various lighting conditions. The model will be validated using experimental data and/or existing VisNav simulations. The tanker UAV is the Maxdrone, supplied by Lockheed Martin Aeronautics. State-space models of the Maxdrone will be modified to include the effects of the triangle boom assembly, a specialized refueling drogue for refueling small air vehicles. If necessary, an autopilot that will allow the tanker to maintain steady, level 1-g trim during the docking maneuver will be developed. The Boeing Air Dominator is the reciever UAV, and state-space models based on data supplied by Boeing will be incorporated in the simulation. A flight controller which incorporates measurements from the VisNav relative navigation sensor will be synthesized for the end-game docking maneuver, and implemented on the Air Dominator.

    Presently there are two control structures that have been designed and simulated for AAR. The first is Nonzero Setpoint (NZSP), which enables the receiver vehicle to dock with a stationary refueling target. This controller will be used in the Phase I ground-to-ground test, where a robotic mobile platform carrying VisNav equipment will dock with a port on the laboratory wall. The second control structure is the Proportional Integral Filter Command Generator Tracker with Control Rate Weighting (PIF-CGT-CRW) developed by Kimmett and Valasek for the Autonomous Aerial Refueling of Unmanned Air Vehicles program presented below. This control structure allows the receiver vehicle to track a pre-defined trajectory of the refueling boom.

    A set of scenarios will be created to test the operation of the AAR system in different operating conditions involving various tanker and receiver vehicle relative range and velocities, various lighting conditions, and disturbance effects. These scenarios will first be evaluated by simulation, and ultimately flown.

    Working with me on this program are Graduate Research Assistants:

    • Roshawn E. Bowers
    • Changwha Cho

    and Undergraduate Research Assistants:

    • Zach Reeder
    • Kyle Schroeder
  27. Institute for Intelligent Bio-Nano Materials and Structures for Aerospace Vehicles

    NASA Langley Research Center
    1 September 2002 - 31 August 2007
    Co-P.I.s John L. Junkins, Dimitris Lagoudas, Othon K. Rediniotis, John D. Whitcomb, and James Boyd
    Total award $15,760,418

    NASA has chosen Texas A&M University to lead the Texas Institute of Intelligent Bio-Nano Materials and Structures for Aerospace Vehicles (TiiMS), bringing together some of the top researchers in Texas and the world — including a Nobel laureate and several members of the National Academies — in biotechnology, nanotechnology, biomaterials and aerospace engineering to develop the next generation of bio-nano materials and structures for aerospace vehicles. The technical scope for the institute focuses on basic research issues underlying the major theme of TiiMS — the marriage of biotechnology with nanotechnology to enable the development of intelligent reconfigurable aerospace structures.

    The main focus of TiiMS is to develop and advance the nano and biotechnologies that enable our vision of adaptive, intelligent, shape-controllable micro and macro structures, for advanced aircraft and space systems. The key is integrating intelligence and multifunctionality into the varied components of aerospace systems and vehicles. Our research seeks to investigate and develop advanced control systems to enable intelligence, agility and adaptability of aerospace vehicles made from these smart materials.

    Research Objective 1: Characterization of Shape Memory Alloys using an Artificial Intelligence approach. The capability to control shape modifications of Shape Memory Alloy materials benefits from accurate models of the voltage/current-force/deformation relationships. These models are typically developed from a constitutive relation for the Shape Memory Alloy behavior which is then integrating into a structural model. The characterization approach used here does not need a constitutive model, but uses Reinforcement Learning to directly learn an input-output mapping characterization from physical experimentation, in real-time. This has the potential to significantly simplify and speed up the characterizion process. Adaptive-Reinforcement Learning Control (A-RLC), a computational method that we created and developed, is being used to bridge the gap from numerical simulation to physical experimentation. We have designed and built a bench-test rig to validate the approach, and besides characterization, an optimal control policy is determined that learns how to control the shape of a Shape Memory Alloy to a specified length.

    The results of this Objective are expected to aid in characterizing the effectiveness of this type of advanced control mechanism in intelligent systems, and further research in the modeling and control of morphing air and space vehicles.

    Objective 1 goals:

    • Determine the length of time that is required for the A-RLC unit to learn the nonlinear model of the actual material that will be employed in a morphing wing.
    • Determine the A-RLC unit's ability to optimize state-value functions and minimize a cost function of trajectories after having already learned the SMA model while not having had experience of the particular set of states required.
    • Compare the A-RLC unit's deduction of SMA behavior with current mathematical models of SMA behavior.
    • Generate data that will allow future modification of the A-RLC unit to more quickly optimize the behavior of SMA actuators.

    Research Objective 2: Intelligent shape changing control of morphing air vehicles that use distributed actuation and sensing on a massive scale. The Defense Advanced Research Projects Agency (DARPA) uses the definition of an air vehicle that is able to change its state substantially (to the order of 50% more wing area or wing span and chord) to adapt to changing mission environments, thereby providing superior system capability that is not possible without reconfiguration. We are developing an Adaptive-Reinforcement Learning Control (A-RLC) methodology to the problem of Morphing for Mission Adaptation. A-RLC is a marriage of traditional feedback control and Artifcial Intelligence intended to address two of the three essential functionalities for a morphing vehicle: how to reconfigure, and learning to reconfigure. The third is knowing when to reconfigure. A-RLC uses Structured Adaptive Model Inversion (SAMI) as the controller for tracking trajectories and handling time-varying properties, parametric uncertainties, un-modeled dynamics, and disturbances. Reinforcement Learning with a Q-Learning algorithm is used to learn how to produce the optimal shape at every flight condition over the life of the aircraft. Important aspects of nonlinear, massively distributed actuation and sensing systems are control effector saturation and stability. We are developing a rigorous theoretical framework that addresses these aspects. The A-RLC methodology will be demonstrated with a numerical simultation example of 3-D delta wing unmanned air vehicle that can morph in all three spatial dimensions, over a set of optimal shapes corresponding to specified flight conditions, while tracking a specified trajectory in the presence of disturbances.

    Objective 2 goals:

    • Modeling and control of hierarchical adaptive systems.
    • Distributed sensing, actuation and intelligence.
    • Applications at different length scales.

    Research Objective 3: Space Based Radar (SBR) antenna reconfiguration by morphing. The state of the art in spacecraft communication requires that multiple antennas be mounted on a single spacecraft so as to permit communication with multiple ground stations, many of which have unique receivers and transmitter characteristics. Our approach is to use a single antenna capable of altering its geometry to achieve world-wide compatibility between receivers and transmitters. We seek to develop and demonstrate the feasibility of a reconfigurable antenna shape controller that can achieve and control the optimal antenna shape, on demand. Shape-Memory Alloys (SMA) have been employed to enhance structural properties and increase the ability of structures to adapt and conform as desired, and antenna elements rigged with SMA actuators will be used here as the actuation element. Adaptive-Reinforcement Learning Control A-RLC) will be used to efficiently alter the antenna shape to achieve optimal concavity. This controller is capable of independently learning the optimal concavity in a lifelong sense, thus allowing a space-based radar and communication systems to decrease the quantity of antennas currently mounted on spacecraft.

    Objective 3 goals:

    • Synthesize an original Reinforcement Learning algorithm.
    • Construct a simple finite element model of a parabolic antenna element.
    • Quantify input/output behavior of a space antenna utilizing SMA actuators.
    • Demonstrate reconfiguration capability using simulation.

    Animation Files That Can Be Downloaded

    These simulations demonstrate reinforcement learning. The goal is for a rectanguler shaped object named Timmy to apply Reinforcement Learning in order negotiate an obstacle course of similarly shaped gates, at various heights and orientations. The only actions the object is permitted to use are vertical displacements and 45 degree rotations. The first file (Timmy1a) shows Timmy's performance the first time he attempts to negotiate the obstacel course. The second file (Timmy1b) shows his performance the second time. Timmy was then subjected to a brand new obstacle course, where he was only allowed to use the knowledge he had learned from two passes through the other obstacle course. This is the subject of the third file (Timmy1c).

    The second set of files represent a 3-D air vehicle version of Timmy which uses the A-RLC arhitecture. For a constant volume, Timmy is permitted to morph in the y and z dimensions while tracking a specified trajectory. These more realistic simulations contain uncertain aerodynamic drag, uncertain mass, and uncertain and time-varying inertias (due to morphing). The first is for a parallelopiped vehicle, and the second for an ellipsoidal vehicle.

    Animation Files That Can Be Downloaded

    Working with me on this program are Graduate Research Assistants:

    • Monish Tandale
    • Jie Rong
    • Paul Gesting
    • James Doebbler
    • Theresa Spaeth

    and Undergraduate Research Assistants:

    • Chris Haag
    • Holly Feldman
  28. Intelligent Vision Sensing For Motion Based Guidance

    State of Texas Advanced Research Program, Austin, TX
    1 January 2002 - 31 December 2003
    Co-P.I. John L. Junkins
    Total award $240,000NASA Langley Research Center

    VisNav Glove Flight System

    Air vehicles have always required numerous hours of pilot training to obtain a sufficient level of competence. Most people can understand the pitching, rolling, and yawing motions of an airplane by simply watching them fly. However translating these motions into control stick, throttle, and rudder petal movements is much less intuitive. This research focuses on the development of a new glove-based input device, utilizing the revolutionary Vision Based Navigation system, developed at Texas A&M, called VisNav. This data glove type interface is designed to enable the average person to command and fly an aircraft, using only hand motions. This is a very intuitive and natural way to pilot an airplane, and requires very little specialized training. It is a particularly useful capability for rapid prototyping and evaluation of flight control concepts at real-time flight simulator facilities. This concept can also be extended outside of the simulator to allow for remote control of semi-autonomous unmanned aerial vehicles.

    Over the last five years, the Aerospace Engineering Department at Texas A&M University has been researching and developing an intelligent Vision Based Navigation system called VisNav. The VisNav system comprises a new kind of optical sensor combined with structured active light sources (beacons) to achieve a selective or "intelligent" vision. Light is structured in the frequency domain, analogous to radar, so that discrimination and target identification is near-trivial even in a noisy ambient environment. We have applied this technology to the problems of autonomous docking and rendezvous of spacecraft (NASA Johnson Space Center), autonomous landing of UAV's on ships (Office of Naval Research), and autonomous aerial refueling of UAV's (Army Research Office). Essentially, anywhere that extremely accurate relative position and flight rate information is needed with miniaturized equipment.

    Specific tasks and research objectives:

    • Identify the technology factors and requirements for extending the basic VisNav technology.
    • Use these technology factors and requirements to enable development of a VisNav wireless data glove for the remote control of vehicles using hand motions and gestures.
    • Demonstrate real-time operation of the data glove for controlling a high fidelity, real-time, flight simulator.

    Working with me on this program are Graduate Research Assistants:

    • Brian Wood
    • Roshawn Bowers
    • Yuanyuan Ding
  29. Cooperative and Formation Control of Autonomous Vehicles

    Army Research Office through a National Defense Science and Engineering Graduate Fellowship (NDSEG)
    1 September 2001 - 31 August 2004

    Cooperative and formation control of autonomous land, air, and underwater vehicles is an emerging technology area with a seemingly endless array of military and civil applications. In the case of air vehicles, autonomous formation control will provide enhanced tactical effectiveness and a large reduction in aerodynamic drag due to a change in the direction of the lift vector due to the upwash of the lead aircraft. This effect is similar to the familiar "drafting" in automotive racing. Aircraft formation flight control research in the Flight Simulation Laboratory at Texas A&M University is aimed at designing, testing, and evaluating robust, stable control algorithms to be used in both manned and unmanned aircraft formation control applications.

    Our approach is to use a structural dynamics analogy for an unconstrained formation of generic vehicles in one or two dimensions. For individual vehicles, this framework consists of formulating the equations of motion with virtual dampers and springs, and then using feedback linearization to eliminate nonlinearities and drive errors asymptotically to zero. A virtual formation is used to control the trajectory of all vehicles within the formation, by specifying the desired trajectory of its center of mass.

    Specific tasks and research objectives:

    • Formulate the governing equations so that the least amount of data flow as possible is required between vehicles.
    • Determine the requirements on accuracy for practical application to flight vehicles.
    • Integrate the formation controller with the Fault Tolerant Structured Adaptive Model Inversion (SAMI) control methodology.
    • Test and evaluate the control algorithms via non real-time and real time simulation.
    • Investigate schemes for cooperative control of vehicles in formation.

    Future phases of this project will encompass nonlinear control design methods, and integrate the Vision Based Navigation (VisNav) relative positioning system to provide accurate relative position measurements in real-time. Formation flight testing of the algorithms will be conducted at the Flight Test Facility of the Texas A&M Flight Mechanics Laboratory, using the Maxdrone research UAV.

    Animation Files That Can Be Downloaded

    In this simulation, the horizontal axis represents time and the vertical axis represents the inertial position of the masses in one-dimensional space. Five masses are located at negative locations in the inertial axes. They are all spaced, initially, at 1 unit length apart. The desired trajectory of the center of mass is a sinusoidal function, and the desired spacing of the masses is 2 unit lengths. At the initial time ##t=0##, the control forces are applied. It is seen in the simulation that the masses move in a sinusoidal fashion, after the transient response has decayed, keeping their spacing constant as desired.

    This simulation demonstrates the ability of the algorithms developed to handle time-changing spacing between the masses. The horizontal axis represents time and the vertical axis represents the inertial position of a mass in one-dimensional space. Two masses are initially located at "erroneous" positions. The controller is turned on and commands the masses to move the mass center of the formation to the inertial position of zero. Additionally, however, the spacing between the masses is to be a sinusoidal function of time.

    In this simulation, the horizontal axis represents the ##x## inertial location of three masses, and the vertical axis represents the ##y## inertial location of the masses. The masses are initially in an undesired configuration at an undesired mass center location. At the initial time ##t=0##, control is applied commanding the masses to go into a desired "inverted v" formation, and to track a search pattern. The masses maintain their desired relative position and are able to track the desired pattern perfectly after the transients have decayed.

    Working with me on this program is Graduate Research Assistant:

    • Edward R. Caicedo
  30. Autonomous Aerial Refueling of Unmanned Air Vehicles

    Army Research Office through a National Defense Science and Engineering Graduate Fellowship
    1 September 2001 - 31 August 2004

    Unmanned Aerial Vehicles (UAV's) have many important applications ranging from military to research and everyday civilian uses. The goal of this research is to extend the operational envelope of UAV's by designing an autonomous in-flight refueling system. One of the most difficult technological hurtles to overcome in autonomous in-flight refueling is the need for a highly accurate sensor to measure the locations of the tanker and the aircraft. Currently GPS is limited by an approximately one-foot accuracy.

    This project overcomes the sensor accuracy problem by utilizing a revolutionary Vision-based Navigation system called VisNav. Since 1998, Texas A&M researchers have been developing a revolutionary vision system that accurately determines the line of sight vector between two objects, to an accuracy of millimeters. It is capable of providing the needed six degree-of-freedom information for real-time navigation, and can enable accurate autonomous aerial refueling without extensive alterations in the current refueling systems. It can be applied to both the current probe-and-drogue as well as the boom method for refueling. VisNav comprises a new kind of optical sensor combined with structured active light sources (beacons) to achieve a selective or "intelligent" vision. VisNav structures light in the frequency domain, analogous to radar, so that discrimination and target identification is near-trivial even in a noisy ambient environment. Several Light Emitting Diodes (LED) called beacons, are attached to the refueling target frame ##A##, and an optical sensor, or Position Sensing Diode (PSD), on the aircraft frame ##B##.

    The LEDs emit structured light modulated with a known waveform; filtering of the received energy allows all ambient energy to be ignored. Thus VisNAv can be used in 100 percent cloud cover, total darkness, and adverse weather conditions. The position of the light centroid on the PSD is directly related to the centroid of the beacons with respect to the location of the PSD on the aircraft. A Gaussian Least Squares Differential Correction (GLSDC) routine is used to calculate the six-degree of freedom data at an update rate as high as 100 Hz.

    Working with me on this program is Graduate Research Assistant:

    • Jennifer J. Kimmett
  31. Development of an Integrated Multidisciplinary Curriculum for Intelligent Systems

    National Science Foundation
    1 March 2001 - 29 February 2004
    Co-P.I.s Dimitris C. Lagoudas, Thomas W. Strganac, Othon K. Rediniotis, and John D. Whitcomb
    Total award $354,999

    This program is a curriculum development in the Aerospace Engineering department which provides undergraduate students an optional degree specialization in intelligent systems, encompassing both specialized core courses and elective courses throughout the freshman through senior years. Each participant receives specialized instruction in intelligent autonomous vehicles; biomimetics; smart materials technology; fluid-structure-control interactions; multidisciplinary design optimization; computational mechanics; controls; aerodynamics; and structures. The capstone of this program is a two-semester senior design sequence in which students design, simulate, test, build, and fly intelligently controlled uninhabited aerial vehicles (UAVs). Students who complete this Intelligent Systems option receive a certificate recognizing their accomplishment.

    Working with me on this program are Graduate Research Assistants:

    • Brian Wood
    • Monish Tandale
    • Roshawn Bowers
  32. Flight Tests of an Unmanned Powered Parachute: A Validation Tool for GN&C Algorithms

    Advanced Mission Design Branch, NASA Johnson Space Center
    1 September 2000 - 31 December 2001
    Co-P.I.s Donald T. Ward, Thomas C. Pollock, and David W. Lund
    Total award $219,534

    Paratows  buckeyes

    The NASA X-38 is the prototype of a Crew Return Vehicle (CRV) which will be used as an emergency escape system or lifeboat from the International Space Station. The X-38 and CRV are some of the first re-entry vehicles to use a parafoil for maneuvering during the terminal phase of its operational trajectory. The CRV will operate with a high degree of autonomy, and its guidance algorithms must be able to avoid obstacles in the landing area, and permit touch down with a rate of descent that will not harm the vehicle occupants, who could be injured or otherwise incapacitated.

    To exercise the guidance algorithms and serve as a testbed for the X-38, NASA purchased two Buckeye "powered parachutes". One vehicle was configured and instrumented to fly autonomously. To assist with the modeling of this vehicle and validation of the guidance algorithms and instrumentation package, tow tests of the X-38 parafoil system (above left) and flight tests of the Buckeye vehicle (above right) will be conducted at the Flight Test Facilityof the Texas A&M Flight Mechanics Laboratory.

    Specific tasks and research objectives:

    • Assess the ability of various guidance algorithms to be flown on V201 to maintain heading control.
    • Measure the targeting capability of algorithms for V201 use.
    • Validate wind alignment and estimation performance attained by the guidance algorithms.
    • Quantify navigational errors (including actual deviations from the desired trajectory, biases, noise, etc.) acheieved during the simulated terminal phase maneuvering.
    • Compare data from Buckeye flights to simulator predictions of performance and extrapolate the findings to the X-38 vehicle in its terminal maneuvering.
    • Provide a hvehicle for emulating pallet drops (modeling the larger parachute planned for V201) that could use the terminal guidance algorithms.
    • Develop and valdiate the use of the autonomous Buckeye vehicle as a hazard avoidance testbed.

    Working with me on this program are Graduate Research Assistants:

    • Gi-Bong Hur
    • Dallas Hopper
    • Edward R. Caicedo
  33. Synthesis and Evaluation of Robust Dynamic Inversion Flight Controllers for X-38 Class Re-Entry Vehicles

    GN&C Design and Analysis Branch, NASA Johnson Space Center
    1 May 2000 - 1 May 2001
    Total award $97,320

    X-38 In Flight Test

    As opposed to traditional synthesis techniques, in which the nonlinear plant is separated into several linearized models at discrete operating points, and a closed-loop controller is synthesized for each one, Dynamic Inversion seeks to synthesize a global control law from a single nonlinear model. Two open research issues are the "user friendliness" of designing Dynamic Inversion controllers, and controller robustness and fragility.

    A previous Dynamic Inversion study on the X-38 conducted at Texas A&M (see below) partially addressed the first of these open issues by generating a comprehensive design guidelines document complete with tutorials, procedures, tools, and examples.

    With regard to the second issue, Dynamic Inversion by itself cannot assure stability and performance robustness to disturbances and perturbations in the plant and controller. Therefore, an additional robust control technique must be married to the Dynamic Inversion controller to ensure robustness. There are several robust control techniques and robustness measures currently available to the control designer. Examples in the current literature show a tendency to use whatever robust control and analysis techniques the designer is most familiar with, as opposed to those which are best for a particular application. H-infinity and Mu-synthesis are two of the more popular techniques.

    Specific tasks and research objectives:

    • Demonstrate practical application of the guidelines, procedures, tools, and software previously developed, and validate the design guidelines document. This will be done with a Dynamic Inversion controller design case study for a re-entry vehicle.
    • Identify and evaluate the advantages that European Dynamic Inversion methods have to offer in terms of ease of use, and suitability for implementation, compared to the particular Dynamic Inversion approach commonly used in North America. These advantages will be directly incorporated into the comprehensive design methodology.
    • Develop new, non-conservative robustness measures, and examine the fragility of Dynamic Inversion control laws.

    Working with me on this program are Graduate Research Assistants:

    • Jennifer A. Georgie
    • Dai Ito
  34. Cockpit Data Fusion with Fixed-Base Simulation Validation for Free-Flight Guidance

    State of Texas Advanced Technology Program, Austin, TX
    1 January 2000 - 31 December 2001
    Co-P.I. John H. Painter
    Total award $208,061

    Texas A&M Flight Simulation Laboratory

    This research aims to solve a fundamental technical problem associated with civil aviation moving operationally into Free Flight, the new air traffic management paradigm intended to make the nation's air traffic control system safer and more efficient. Presently, air traffic is managed through ground tracking, ground computing, and verbal negotiations between ground controller and pilot. Conceptually, Free Flight allows a pilot significant latitude to optimize a flight trajectory, as it is being flown. An important ramification, especially for General Aviation, is that responsibility for aircraft separation will rest increasingly with the pilot. The entire Free Flight scheme relies on greatly increased digital data flow between pilot, ground controller, and between all aircraft in the immediate airspace. Onboard computing then uses this collected data to optimize individual aircraft guidance.

    GAPATS Display

    Technically, the research problems are those of cockpit data fusion onboard the aircraft, and of computational and visual aids for pilot and ground controller. Our approach is to extend our previous work in independent flight software agents, to the development of a single high dimensional "Arbitrator" agent. The Arbitrator will resolve conflicts between the guidance vectors produced by several independent agents. Data fusion software elements will be developed and implemented into flight software, and a real-time, fixed-base flight simulator will be used for validation.

    Weather Radar Image. CLL is for Easterwood Airport, College Station, TX

    The present research is based on five years of prior research in this area funded by NASA Langley Research Center, the State of Texas, and Rockwell/Collins. Some of the pilot decision aiding capabilities created and developed were an Independent Approach Monitor and an Independent Weather Agent. Many of the prior results will also be incorporated, including a new pilot decision aid based on Fuzzy Logic (patent applied for), and an integrated cockpit computation and display system, employing expert systems, for aiding the pilot in instrument flying, known as the General Aviation Pilot Advisor and Training System (GAPATS).

    Specific tasks and research objectives:

    • Resolve trajectory guidance conflict resolution by implementing a suitable guidance software architecture and requisite algorithms, with validation by fixed-base flight simulation, under Free Flight conditions. This is the key technology item for enabling individual aircraft to compute and fly trajectories while simultaneously maintaining separation from other data-linked aircraft, from weather, and from terrain. Achievement of this objective is greatly aided by the existing fixed-base flight simulator, having the augmentable cockpit software system produced under the previous NASA Langley GAPATS project.
    • Simulator validation of the conflict resolution guidance software, specifically with regard to handling traffic restrictions for scenarios with multiple aircraft. This entails generating the multiple traffic trajectories that are to be digitally communicated to the aircraft, according to the Automatic Dependent Surveillance Broadcast (ADS-B) format and scenarios.
    • Simulator validation of the weather restrictions guidance software, for the conditions of squall line weather. Simulated radar intensity data for a moving line of thunderstorms will be generated, and this intensity data will be integrated into the existing moving map display, and into the existing simulator weather graphic, as seen from the cockpit.

    Working with me on this program are Graduate Research Assistants:

    • C. Cale Stephens
    • Surya U. Shandy
    • Jie Rong
    • Sangeeta Bokadia
    • Dallas Hopper

    and Undergraduate Research Assistants:

    • Kristi Ferber
    • Heather Ransom
    • Theresa Spaeth
    • Nicole Norstrud
  35. Display Automation and Assessment Concepts for an Advanced Tactical Airlift Cockpit

    Marconi Aerospace Defense Systems Inc., Austin, TX
    15 October 1999 - 15 April 2000
    Co-P.I.'s John H. Painter and Donald T. Ward
    Total award $98,326

    Royal Air Force C-130J Flight Deck

    The United States Air Force is planning to modify approximately 525 aircraft to establish a common, supportable, cost effective baseline configuration for AMC, ACC, ANG, AFRC, PACAF, USAFE and AFSOC C-130 aircraft. The selected contractor will design, develop, integrate, test, fabricate and install a new avionics suite for approxi-mately thirteen variants of C-130 Combat Delivery and Special Mission models. The installation schedule requires a throughput of between 65 and 85 aircraft per year through 2010.

    This research will study advanced display integration concepts for reducing crew workload, and improving situational awareness for the tactical airlift mission. It is an element in the risk reduction effort in conjunction with the Marconi-Honeywell effort for the C-130 Avionics Modernization Program (C-130 AMP).

    Specific tasks include:

    • Analyzing the impact of current human factors requirements and developing metrics to allow evaluation of the effect of avionics improvements on individual and collective crew performance
    • Proposing tasks for cockpit automation that are likely to promote the two-man crew concept.
    • Evaluating potential contributions to rapid prototyping by the Texas A&M Flight Simulation Laboratory and its real-time, fixed-base flight simulator.

    Working with me on this program is Graduate Research Assistant:

    • C. Cale Stephens
    • Jennifer A. Georgie

    and Undergraduate Research Assistants:

    • Kristi Ferber
    • Dallas Hopper
  36. Flight Evaluation of Prototype Optical Landing System

    Cockpit Computer Corporation, Inc., Saratoga, CA
    1 March 1999 - 30 September 1999
    Co-P.I.'s Donald T. Ward and Thomas C. Pollock
    Total award $51,241

    Block diagram of video landing system

    This is a flight test research and demonstration program utilizing the Grumman American Commander 700 aircraft of the Texas A&M Flight Mechanics Laboratory Flight Test Facility. It is a cooperative effort between Cockpit Computer Corporation, and Texas A&M University, to develop an affordable video-based position sensor and associated cockpit display that should significantly improve the landing precision and safety of the new breed of high performance single-pilot General Aviation (GA) airplanes.

    During the latter stages of an approach and landing, guidance commands for the initial approach segment, final approach segment, flare segment, and touchdown segment are displayed to the pilot as the approach is executed. The required accuracy is provided from six degree-of-freedom information processed from forward- and down-looking video imagery, integrated with GPS position data and a 3D Graphic Synthetic Vision Generator. An onboard database contains accurate position coordinates of runways, obstacles, and terrain.

    This guidance scheme, even for VFR operations, has the potential to draw larger numbers of relatively low-time pilots into the GA aircraft market, while keeping accident rates at an acceptable level. With this system, relatively low time GA pilots should be able to land more precisely (on airspeed and at the desired touchdown point) than without the system.

    Working with me on this program is Graduate Research Assistants:

    • Jennifer A. Georgie
    • Surya U. Shandy
  37. Evaluation of Dynamic Inversion as a Flight Control Methodology for Re-entry Vehicles

    GN&C Design and Analysis Branch, NASA Johnson Space Center
    16 February 1999 - 16 February 2000
    Co-P.I. Donald T. Ward
    Total award $64,307

    x38  x38

    Pictured above is the NASA X-38 Crew Return Vehicle (CRV), also known as the Lifeboat in Space. The CRV will provide personnel on the International Space Station with the capability to safely return to Earth in the event of an emergency. As currently designed it will carry seven people, and will be flown autonomously, i.e. no one on board need be a pilot to safely land it.

    One of the flight control methodologies which will permit this capability is Dynamic Inversion. Also called Feedback Linearization, it is a non-traditional methodology for synthesizing closed-loop control laws. As opposed to traditional techniques whereby the nonlinear plant is separated into several linearized models at discrete operating points and a closed-loop controller is synthesized for each one, Dynamic Inversion seeks to synthesize a global control law from a single nonlinear model. It has been applied to paper studies of controllers for aircraft such as the F-18 HARV, and has been flown successfully on the X-36.

    Specific questions to be answered by this research are:

    • Is the methodology suitable for a flight vehicle with an extreme range of operating conditions (hypersonic-supersonic-transonic-subsonic) like the X-38?
    • Is the method suitable for rapid prototyping? Specifically, is software validation of the resulting control laws straightforward and rapid?
    • For which type of applications and in what circumstances (range of operating conditions or flight regimes) is output feedback suitable as opposed to full-state feedback?
    • Is it sufficiently robust to handle flight vehicle uncertainties (aerodynamics and mass properties), atmospheric distrurbances, and effector failures?

    Working with me on this program is Graduate Research Assistant:

    • Dai Ito
  38. Real Time Adaptive Navigation and Control of Highly Nonlinear Autonomous Systems

    United States Navy Office of Naval Research
    1 July 1997 - 30 June 2000
    Co-P.I.'s John L. Junkins and Donald T. Ward
    Total award $563,649

    navy ucav

    The goal of this program is to investigate novel and highly advanced technologies which will enable autonomous systems with high levels of uncertainty in the presence of noise and unbounded disturbances to achieve breakthrough combat capabilities in future high threat environments. Specific enabling technologies being researched by the collective Texas A&M team on this program include Shape Memory Alloy (SMA) control effector actuation, vision based automatic landing systems, and intelligent autonomous flight controllers. The broad class of system includes autonomous underwater vehicles, robotic land vehicles, and Unmanned Combat Aerial Vehicles (UCAV) such as the type pictured above, which is the vehicle type for this research.

    Specific topic areas being researched include:

    • Robust nonlinear adaptive control.
    • Online, real-time, nonlinear system identification in the presence of noise.
    • Intelligent flight directors.
    • Extremal mapping.
    • Fighter agility metrics for UCAV's.

    Working with me on this program are Graduate Research Assistants:

    • Wei Chen
    • Praveen Joshi
    • David M. Smith
    ucav chart
  39. Multi-Axis Pneumatic Vortex Control

    Air Force Office of Scientific Research
    Air Force Research Laboratory
    Total monies awarded as stipend while in-residence

    Advanced Pneumatic Vortex Control For Aircraft

    Texas A&M University Research Enhancement Program
    Total award $7,500


    f-16xl  x-29

    Pneumatic Vortex Control (PVC) is concerned with generating controlling forces and moments on aircraft by injecting small jets of gas (such as nitrogen or air) into the vehicle flowfield. The jets create vortices which, by the Von Kármán effect, reduce local pressures, thereby generating forces. Early full-scale research and flight testing used PVC on the X-29A (right) to generate forebody vortices for yaw control at high angles-of-attack. These tests validated the PVC concept, and subsequent research developed Model Predictive Variable Structure Controllers (MPVSC) and Fuzzy Logic Controllers for this aircraft. The ultimate expression of the PVC concept is full pneumatic control at high speeds and low angles-of-attack. This would be characterized by engine bleed air supplied PVC devices on the forebody, wing, and vertical tail completely replacing elevators, ailerons and rudders. Current research is focused upon extending the controllers developed for the X-29A, and developing new Neural Controllers for multi-axis PVC control of the F-16XL (left).

    Working with me on this project are Graduate Research Assistants:

    • Praveen Joshi
    • Dai Ito
  40. Coupled Static and Dynamic Stability of Aircraft

    Based Upon Previous Work By Juri Kalviste

    The stability of aircraft is usually expressed in terms of both static stability criteria (e.g., ##C_{m_{\alpha}} < 0##), and dynamic stability criteria (e.g., ##\zeta_{D.R.} > 0##). These criteria are normally evaulated with steady, linear aerodynamic data. The results are adequate for low angle-of-attack, light maneuvering flight regimes (where aircraft spend the majority of their flight time). In heavy maneuvering, high angle-of-attack flight regimes, the aerodynamic data tends to be unsteady and nonlinear, whereby these stability criteria are no longer valid. A set of new stability parameters are sought for analysis of aircraft stability throughout the flight envelope. These parameters will define aircraft stability based on the aircraft's aerodynamic and inertial properties, and will include both static and dynamic effects, inertial coupling, and kinematic coupling effects. A method of relating these parameters to the conventional stability modes of an aircraft is sought, in order to isolate the formation of new dynamic modes due to coupling.

  41. Novel Methods To Replace Mechanical Fasteners On Major Aircraft Component Attachments

    Raytheon Aircraft
    1996 - 1997

    PROBLEM: The wing-to-fuselage and empennage-to-fuselage attachment points of conventional aircraft are heavy structures which use large mechanical fasteners such as bolts and rivets. Reduction of complexity and weight in these areas would be beneficial from the standpoints of maintenance (maintenance man-hours-per-flight-hour), supply infrastructure (spare parts and cataloging), and performance (reduced empty weight). For aircraft with major graphite/epoxy type composite components, the problem is more accute since durability and structural integrity tend to be compromised by the prsesence of holes in the component. The holes are necessary attachment points for mechanical fasteners.

    SOLUTION: Use adhesives to bond the composite assemblies, thereby eliminating the need for mechanical fasteners.

    DRAWBACK: Aircraft operate in extreme environmental conditions of moisture, heat, cold, and thermal/mechanical cycling. Adhesive bonds which are durable in such environments are costly, maintenance intensive (re-bonding), and not durable over the life of the aircraft.

    BETTER SOLUTION: Use the adjoining structure to maintain the relative position of components and transfer loads.

    spar  cap

    Specifically, replace the bulky original equipment mechanical attachment (left) with the "capped" arrangement (right). Using the Beechcraft Bonanza Model A36 as a starting point, three candidate configurations have been designed to carry the equivalent flight loads of the existing conventional strucuture:

    • Capped
    • Tongue-In-Groove
    • Dovetail

    The new configurations are made of conventional materials, and were evaluated using Finite Element Methods.

    cap

    We expect to obtain experimental data using laboratory test articles.

    FUTURE DIRECTION: Design graphite/epoxy type replacement attachment structures.

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