People, Innovation, Excellence

Research Goal

Utilize the Theory-Computation-Experiment paradigm to research Low Cost Attritable Aircraft Technology (LCAAT) with autonomy to establish trust, providing a game changing capability that transforms the way manned and unmanned air, space, and ground systems are designed, controlled, and operated to effectively accomplish missions and tasks. VSCL is thus focused on synergistic strategies for the analysis, control, validation & verification of complex autonomous vehicle and sensor systems operating in challenging environments.

The Vehicle Systems & Control Laboratory is directed by Dr. John Valasek.

Graduate Research Assistant Positions Available

The Vehicle Systems & Control Laboratory (VSCL) has multiple fully funded Ph.D. positions in Aerospace Engineering that are available. Interested students are encouraged to apply for research in the following areas:
– Autonomous and Nonlinear Control of Cyber-Physical Air, Space, and Ground Systems
– Vision Based Sensors and Navigation Systems
– Cybersecurity for Air and Space Vehicles
– Air and Space Vehicle Control and Management
– Advanced Cockpit/UAS Systems and Displays
– Control of Bio-Nano Materials and Structures
– Human-in-the-Loop Artificial Intelligence for Coordinated Autonomous Unmanned Air Systems

More information and details for applying can be found here.

UAS Research and Flight Testing by the Numbers

  • 21 Years of Fixed-Wing UAS Flight Testing under FAA Auspices
  • 26 Externally Funded UAS Research Programs (1999 – Present)
  • 400+ Flights with an operational tempo of 133 thermal IR and multi-spectral data collection flights in the field over 12 months (2015 – 2016)
  • 24 Certified UAS Flight Testers Currently on Staff
  • 3 Certified UAS Pilots Currently on Staff
  • 13 UAS Vehicles in Current Fleet

Research Project Spotlight

Project: System Identification for Unmanned Air Systems

Sponsor: National Science Foundation (NSF) Center for Autonomous Air Mobility & Sensing (CAAMS)

Purpose: System Identification is a process to develop a mathematical representation of the dynamics of a physical system from measured data. Accurate models enable prediction of performance and dynamics of a system.

Challenges: Models for sUAS are generally not available as manufacturers do not have models for commercial sUAS and models for military sUAS are not typically available. Modeling and control systems are often vehicle dependent and not easily portable across sUAS. Many commercial autopilots do not provide data needed for online system identification

Our Approach: Utilizing the Observer Kalman Filter Identification algorithm with the Developmental Flight Test Instrumentation 2 framework, full state space models can be identified in near-real time onboard the vehicle utilizing data from a variety of sensors.

Recent News

Bennett Receives Graduate Excellence Fellowship Award

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VSCL Graduate Assistant Researcher, Jillian Bennett, is a recipient of the Graduate Excellence Fellowship Award for Spring 2024. This is a competitive, merit-based fellowship awarded to students by the Aerospace Engineering Graduate Committee. The fellowship includes a $1,000 supplemental award for Spring 2024.

Jillian is a Master of Science student, with a focus in Dynamics & Control. She is currently on the KAMS project, working on adaptive control for multiple time scale systems. She has been in the VSCL since Spring 2023, previously working on flight testing for the System Identification project. Jillian has an interest in flight testing, nonlinear control, and vehicle dynamics.

Two New Graduate Students Join VSCL in Spring 2024

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VSCL is proud to welcome two new graduate research assistants:

Erin Swansen joins VSCL as a Ph.D. transfer student in the Aerospace Engineering department. Erin has over five years of experience in industry at Boeing as a guidance, navigation, and control engineer in the Advanced Autonomous Systems group. Her work involved guidance and control system development for a variety of aerial platforms including UAVs, high performance aircraft, and guided weapons. During graduate school, she has interned at NASA and Sandia National Laboratories doing flight control research and development. Her professional and research background includes significant work using robust and adaptive control to address challenges in flight, particularly for hypersonic vehicles. She has also conducted research sponsored by Sandia National Laboratories to develop a new methodology to improve performance of machine learning algorithms for sparse data sets. Her current research interests focus on implementable and verifiable algorithms that allow the safe use of machine learning in guidance and control architectures. Erin earned a B.S. in Systems Science and Engineering and an M.S. in Electrical Engineering from Washington University in St. Louis. With VSCL, Erin will be contributing towards the Center for Autonomous Air Mobility and Sensing (CAAMS) which is sponsored by the National Science Foundation (NSF).

 

Payton Clem is a Master of Science Student in the Aerospace Engineering department. She is graduating from Texas A&M with her Bachelor of Science in Aerospace Engineering with Minors in Mathematics and Astrophysics in Fall 2023. During her undergrad, she was involved in campus activities like working at the Memorial Student Center to provide support to her fellow Aggies, and was a member of P.S.U.N., an on campus organization that provides free programs and events to children with special needs. Finding an interest in research, she worked in Dr. Daniel Selva’s lab, SEAK, on a NASA SBIR project with Aureus Innovation to develop a new systems engineering language. This involved creating a satellite design from scratch using systems engineering diagrams with the SIMPL developing language. Within the SEAK lab she also assisted in developing a rule based planner that would be used in a space mission simulation for space mission design. She was also the project lead of her capstone design group, which provided a satellite constellation design, as well as mission planning software to aid in the solution of an on-orbit servicing problem for L3Harris. As she continued her research, Payton developed an interest into the applications of artificial intelligence within the aerospace engineering field. Payton became a member of VSCL in her senior year, applying her interest in AI by working on the Robust Threat Detection project, research she will continue during her Masters. Her work with VSCL will be primarily focused on Autonomous, Nonlinear Control of Air, Space and Ground Systems.

Lehman and Valasek Publish “Design, Selection, Evaluation of Reinforcement Learning Single Agents for Ground Target Tracking,” in Journal of Aerospace Information Systems

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Ph.D. student Hannah Lehman and Dr. John Valasek of VSCL published the paper “Design, Selection, Evaluation of Reinforcement Learning Single Agents for Ground Target Tracking,” in Journal of Aerospace Information Systems.  

Previous approaches for small fixed-wing unmanned air systems that carry strapdown rather than gimbaled cameras achieved satisfactory ground object tracking performance using both standard and deep reinforcement learning algorithms. However, these approaches have significant restrictions and abstractions to the dynamics of the vehicle such as constant airspeed and constant altitude because the number of states and actions were necessarily limited.  Thus extensive tuning was required to obtain good tracking performance. The expansion from four state-action degrees-of-freedom to 15 enabled the agent to exploit previous reward functions which produced novel, yet undesirable emergent behavior. This paper investigates the causes of, and various potential solutions to, undesirable emergent behavior in the ground target tracking problem. A combination of changes to the environment, reward structure, action space simplification, command rate, and controller implementation provide insight into obtaining stable tracking results. Consideration is given to reward structure selection to mitigate undesirable emergent behavior. Results presented in the paper are on a simulated environment of a single unmanned air system tracking a randomly moving single ground object and show that a soft actor-critic algorithm can produce feasible tracking trajectories without limiting the state-space and action-space provided the environment is properly posed.

This publication is part of VSCL’s ongoing work in the area of Reinforcement Learning and Control.  The early access version of the article can be viewed at https://arc.aiaa.org/journal/jais

VSCL Alumnus Ryan Weisman Awarded Technical Fellow of KBR

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VSCL alumnus Dr. Ryan Weisman ’12 has been inducted as a 2023 Fellow of KBR for his contributions in space situational awareness. Space superiority requires decision-making in ambiguous situations characterized by short timelines, reduced sensing, and conflicting information. Dr. Ryan Weisman’s work increases military space mission resilience to adversary parity, mission anomalies, and unforeseen situations by identifying and enabling operations under less explored, physically possible conditions beyond conventional, probable operating regimes. His operational tools provide warfighters proactive sensing recommendations, situation assessment, and solution confidence directly traceable to physics and data quality for navigation and vehicle safety without excessive data collection or exhaustive simulation.

Co-advised by Dr. John Valasek and Dr. Kyle T. Alfriend, Weisman was a recipient of the Science, Mathematics & Research for Transformation Fellowship (SMART) with the Air Force Research Laboratory, Albuquerque, NM, for which he was employed before joining KBR. KBR delivers science, technology and engineering solutions to governments and companies around the world

Texas A&M University Becomes Founding Partner of New NSF Center for Autonomous Air Mobility and Sensing

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Texas A&M University is a founding partner of the National Science Foundation (NSF) Center for Autonomous Air Mobility and Sensing (CAAMS) along withUniversity of Colorado Boulder (CU), Brigham Young University (BYU), University of Michigan (UM), Penn State University (PSU), and Virginia Tech (VT). The center is organized under the NSF’s Industry-University Cooperative Research Centers program (IUCRC). CAAMS consists of three primary partners: academia, industry, and government. Academic faculty collaborate with industry and government members to promote long-term global competitive research and innovation. They create solutions to the most critical challenges faced in the autonomous industry. Dr. John Valasek serves as the Site Director for Texas A&M University. Texas A&M University faculty associated with CAAMS include: Dr. Moble Benedict, Dr. Manoranjan Majji, Dr. Sivakumar Rathinem, and Dr. Swaroop Darbha.

In conjunction with the CASS Lab at Penn State, directed by Dr. Puneet Singla, VSCL will be working on the project Integration of System Theory with Machine Learning Tools for Data Driven System Identification.  This project integrates system theory with machine learning tools for data driven system identification. The objective is to derive nonlinear dynamical models by employing a unique handshake between linear time varying subspace methods and sparse approximation tools from high fidelity flight simulations and flight experiments.

 

VSCL Ph.D. Student Kameron Eves Receives Two Awards From The Center for the Integration of Research, Teaching, and Learning (CIRTL)

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Ph.D. student Kameron Eves received two awards from the Center for the Integration of Research, Teaching, and Learning (CIRTL), a national organization supported by the National Science Foundation (NSF) with 41 member universities.  Eves received the CIRTL Scholar Certificate which recognizes students who have advanced and disseminated research about evidence-based teaching practices for diverse learners. Eves also received the Bednarz Award which annually recognizes a doctoral student for their superior quality evidence-based teaching research and for the depth of their involvement at CIRTL. He received these awards for his work in the Teaching-as-Research (TAR) CIRTL program which supports aspiring faculty who perform evidenced-based teaching research projects.

Eves credits this instruction and experience with preparing him well for his career faculty position.  “I’m honored to have been selected as the 2023 Texas A&M CIRTL Bednarz award recipient and I’m pleased to have also met the requirements for the CIRTL Scholar award. Participating in CIRTL programs throughout my graduate career significantly altered my perspective on education and the role of teachers. I’m particularly grateful for Dr. Valasek’s role as my advisor and exemplar in this endeavor”

Eves’s project investigated the effects of question phraseology on student participation. Specifically, he examined if lowering the social cost and providing a clear method of response affected the quantity of participation and how that participation varied across demographic groups. During his graduate career, Eves participated in several CIRTL programs including the TAR program and the Massive Open Online Course (MOOC).

Eves graduated from Texas A&M University in May 2023 and is currently an Assistant Professor of Electrical & Computer Engineering at Utah Tech University, St. George, UT.

Lehman, Eves, and Valasek Papers Accepted to 2024 AIAA SciTech Forum and Exposition, Orlando, FL, January, 2024

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VSCL Ph.D. student Hannah Lehman, former Ph.D. student Kameron Eves, and VSCL Director John Valasek have had papers accepted to the 2024 AIAA SciTech Forum and Exposition, Orlando, FL, January, 2024.

Machine Learning Across Different Levels of Auction Based Coordination Hierarchies (Lehman and Valasek)

Machine learning has long been discussed as a candidate for facilitating autonomous multiagent vehicle coordination. Many methods of autonomous multiagent coordination have been proposed, however few if any solutions take into account realistic communication challenges. By using machine learning on multiple levels, and a self organizing hierarchical system, an autonomous, pseudo decentralized, heterogeneous, system can dynamically complete tasks without being fully connected. This method will be investigated and demonstrated on a simple, proof of concept rotorcraft simulation.

This publication is part of VSCL’s ongoing work in the area of Tightly Integrated Navigation and Guidance for Multiple Autonomous Agents https://vscl.tamu.edu/research/tightly-integrated-navigation-and-guidance-for-multiple-autonomous-agents-2/

Inlet Unstart Prevention by Adaptive Regulation Using a Nonlinear Longitudinal Timescale Model (Eves and Valasek)

Inlet unstart on hypersonic aircraft causes a rapid and dangerous loss of thrust. Fortunately, proper control design can help prevent inlet unstart. This paper demonstrates how [K]control of Adaptive Multiple timescale Systems (KAMS) can effectively address this challenging problem. First, a multiple-timescale model of a hypersonic aircraft is developed to facilitate the control law design. Then, a KAMS controller is designed using Adaptive Nonlinear Dynamic Inversion to stabilize the reduced subsystems and Sequential Control is used to fuse the control signals for reduced subsystems. The closed-loop system is proven to be stable despite weak non-minimum phase effects. KAMS provides stability guarantees that are more rigorous than prior work and also provides insights into the system’s underlying physics. Numerical results presented in the paper show that KAMS can effectively prevent inlet unstart and mitigate uncertainty using angle-of-attack regulation.

This publication is part of VSCL’s ongoing work in the area of nonlinear multiple time-scale control https://vscl.tamu.edu/research/novel-multiple-time-scale-adaptive-control-for-uncertain-nonlinear-dynamical-systems/

VSCL Awarded Research Contract for Novel Multiple Time Scale Adaptive Control for Uncertain Nonlinear Dynamical Systems by Office of Naval Research

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Dr. John Valasek and the Vehicle Systems & Control Laboratory has been awarded a multi-year (2023-2026) research grant by the Office of Naval Research (ONR) to investigate multiple time scale (MTS) adaptive control systems for naval applications such as unmanned air systems (UAS), high performance aircraft, and satellites. MTS systems are systems with some states that evolve quickly and some states that evolve slowly. These systems can have coupled fast and slow modes which occur simultaneously. MTS systems are particularly interesting from a controls perspective because the time scale separation in the plant can cause degraded performance or even instability under traditional control methods. Accounting for the time scales can remedy this problem. For example, a MTS control technique demonstrated significantly reduced rise times over traditional Nonlinear Dynamic Inversion (NDI). Similarly, traditional adaptive control has been demonstrated to have reduced performance on MTS systems. On the other hand, traditional control techniques that are specifically designed for MTS systems cannot account for systems with model uncertainties. Thus, a method of MTS control for uncertain systems is needed.

A novel methodology called [K]Control of Adaptive MTS Systems (KAMS) is developed which expands upon the class of dynamical systems to which MTS control and adaptive control can apply. While other techniques use elements of adaptive control and MTS control, other research stops short of fully and rigorously combining them. KAMS is a significant improvement over prior methods and provides insight into the physics of the system. It is capable of controlling systems with model uncertainty unlike traditional MTS control, and is robust to systems with unstable zeros unlike traditional adaptive control and feedback linearization.

In addition to investigating theoretical research questions for KAMS, hardware validation of the resulting theory will be performed with a flight testing evaluation campaign using a small unmanned air system (UAS), both fixed-wing and rotorcraft, operating in a challenging environment.

More details of the benefits of KAMS and the research objectives for this project can be found here: https://vscl.tamu.edu/research/novel-multiple-time-scale-adaptive-control-for-uncertain-nonlinear-dynamical-systems/

This project is part of VSCL’s ongoing work in the area of Autonomous, Nonlinear Control of Air, Space and Ground Systems

Valasek to give invited talk at the Systems and Control mini-symposia at the 2023 SIAM Conference on Control and Its Applications (CT23)

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Dr. John Valasek, Professor in the Department of Aerospace Engineering at Texas A&M University and Director of the Vehicle Systems & Control Laboratory, will give an invited talk titled “Multiple-Time-Scale Nonlinear Output Feedback Control of High Performance Aircraft, “ on 25 July 2023 for the Systems and Control mini-symposia at the SIAM Conference on Control and Its Applications (CT23), Philadelphia, PA, July 24-26, 2023.

Abstract:  The Geometric Singular Perturbation theory (Fenichel, 1979) is a powerful control law development tool for multiple-timescale systems because it provides physical insight into the evolution of the states in more than one timescale. The behaviour of the full-order system can be approximated by the slow subsystem, provided that the fast states can be stabilised on an equilibrium manifold. The fast subsystem describes how the fast states evolve from their initial conditions to their equilibrium trajectory or the manifold. This presentation develops two nonlinear, multiple-time-scale, output feedback tracking controllers for a class of nonlinear, nonstandard systems with slow and fast states, slow and fast actuators, and model uncertainties. The class of systems is motivated by aircraft with uncertain inertias, control derivatives, engine time-constant, and without direct measurement of angle-of-attack and sideslip angle. Each controller is synthesized using time-scale separation, lower-order reduced subsystems, and estimates of unknown parameters and unmeasured states. The update laws are so chosen that errors remain ultimately bounded for the full-order system.

The controllers are simulated on a nonlinear, six-degree-of-freedom, F-16A Fighting Falcon model performing a demanding combined maneuver. The slow state tracker accomplishes the maneuver with less control effort, while the simultaneous slow and fast state tracker does so with a smaller number of gains to tune.

Eves and Valasek Publish “Slow Timescale Adaptive Control for Multiple-Timescale Systems,” in Journal of Guidance, Control, and Dynamics

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Ph.D. student Kameron Eves and Dr. John Valasek of VSCL published the paper “Slow Timescale Adaptive Control for Multiple-Timescale Systems,” in Journal of Guidance, Control, and Dynamics.

Multiple-timescale systems are a noteworthy class of dynamical systems that can be modeled with singularly perturbed differential equations. Adaptive control has not been studied in the context of singularly perturbed plants. This paper introduces and evaluates three methods of adaptive control for multiple-timescale systems. Each method is a framework that is valid for a wide class of adaptive control methods. Full-Order Adaptive Control (FOAC) applies adaptive control to the system as a whole.  It is straightforward but can be sensitive to timescale effects.  Reduced-Order Adaptive Control (ROAC) applies adaptive control to either the fast or slow modes only. This simplifies synthesis but can also constrain the range of valid timescale separation. [K]Control of Adaptive Multiple-timescale Systems (KAMS) fuses two adaptive control signals using multiple-timescale techniques.  KAMS takes advantage of model reduction unlike FOAC, and allows for unstable fast dynamics unlike ROAC. Generalized formal definitions, stability criteria, and examples are developed and presented for each method.  Results presented in the paper for the control of a Boeing 747-100/200 on approach show that [K]Control of Adaptive Multiple-timescale Systems has a desirable blend of performance and robustness because each reduced-order model is stabilized separately.

This publication is part of VSCL’s ongoing work in the area of nonlinear multiple time-scale control.  The early access version of the article can be viewed at https://arc.aiaa.org/doi/full/10.2514/1.G007439