Research

Our research is focused on bridging the scientific gaps between traditional computer science topics and aerospace engineering topics, while achieving a high degree of closure between theory and experiment. We focus on machine learning and multi-agent systems, intelligent autonomous control, nonlinear control theory, vision based navigation systems, fault tolerant adaptive control, and cockpit systems and displays. What sets our work apart is a unique systems approach and an ability to seamlessly integrate different disciplines such as dynamics & control, artificial intelligence, and bio-inspiration. Our body of work integrates these disciplines, creating a lasting impact on technical communities from smart materials to General Aviation flight safety to Unmanned Air Systems (UAS) to guidance, navigation & control theory. Our research has been funded by AFOSR, ARO, ONR, AFRL, ARL, AFC, NSF, NASA, FAA, and industry.

Adaptive Flight Control

Novel Multiple Time Scale Adaptive Control for Uncertain Nonlinear Dynamical Systems

Office of Naval Research

Principal Investigator and Technical Lead

1 May 2023 – 30 April 2026

Total award $597,468

Many naval aerospace systems such as unmanned air systems (UAS), high performance aircraft, and satellites are multiple time scale (MTS) systems. 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. For example, in aircraft the short period mode is fast and the phugoid mode is slow. 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. Further, KAMS is expected to provide the following benefits:

Method can be generalized.
Underlying physics inherent in the time scale separation are evident in the control law. This allows for improved analysis.
Does not suffer from the curse of dimensionality.
Derivation and implementation are simplified.
KAMS is agnostic to the type of adaptive control and MTS control used. This could allow the new technique to take advantage of the most recent research.
Improves performance for some systems by reducing rise time and overshoot compared to prior methods.
Improves robustness to changes in time scale separation.

Figure: KAMS Control Loop Block Diagram

KAMS has low technical maturity but high technical potential. The research plan is to investigate KAMS so that it becomes more mature and closer to implementation on naval systems. This requires a theoretical understanding of the capabilities of KAMS and it’s limitations. In addition to investigating theoretical research questions, 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.

TECHNICAL OBJECTIVES

Evaluate the performance of KAMS compared to other traditional control methods
Identify systems which benefit from KAMS
Evaluate KAMS’s performance on naval systems
Generalize KAMS for multi-input multi-output (MIMO), uncertain, nonlinear, nonstandard, adaptive MTS systems
Identify the stable range for the time scale separation parameter
Identify how KAMS changes when adaptive control is applied to the slow control, the fast control, or both.

Working with me on this program are Research Assistants:

– Ph.D. student Christopher Leshikar (B. S. Aerospace Engineering ‘20, Texas A&M University)

– M.S. student Jillian Bennett (B. S. Aerospace Engineering ‘23, Texas A&M University)

– M.S. student Noah Luna (B. S. Aerospace Engineering ‘23, United States Air Force Academy)

State Constrained Adaptive Flight Control, Phase II

Air Force Research Laboratory, Air Vehicles Directorate

Principal Investigator and Technical Lead

3 February 2017 – 3 May 2018

Total award $85,389

The development of control architectures for hypersonic vehicles presents a significant challenge due to widely varying flight conditions in which these vehicles operate and certain aspects unique to hypersonic flight. One particular safety and operational concern in hypersonic flight is inlet unstart, which not only produce a significant decrease in the thrust but also can lead to loss of control and possibly the loss of the vehicle. One potential flight condition that can cause an inlet unstart is flying at a large angle-of-attack or sideslip angle. In Phase I, a nonlinear dynamic inversion (NDI) adaptive controller was developed with the ability to enforce state constraints in order to restrict the vehicle from approaching these large aerodynamic angles. In addition, due to the challenges associated with equipping hypersonic vehicles with traditional external sensor equipment, an observer-based feedback controller for the longitudinal axis of a generic hypersonic vehicle was developed.

Phase II will investigate a single control framework that consists of an observer-based feedback controller capable of achieving tracking for a full 6 degree-of-freedom hypersonic vehicle model, and an NDI adaptive controller capable of enforcing state constraints without full-state measurements. Additionally, a sampled-data NDI control framework is being developed to not only achieve tracking but also include enforcing state constraints as well. The effect of slower sampling times on the ability to control the aircraft and enforce state constraints will be investigated.

TECHNICAL OBJECTIVES

Develop theory for an observer based feedback controller capable of tracking commands that account for the full coupling dynamics, i.e. along both the longitudinal and lateral/directional axes of the aircraft.
Develop theory for enforcing state constraints in the observer-feedback adaptive dynamic inversion architecture.
Develop, implement and analyze a sampled-data control framework based on the continuous time controller previously developed.

Working with me on this program are Research Assistants:

Douglas Famularo, Ph.D student
Sean Whitney, B.S. student

State Constrained Adaptive Flight Control, Phase I

Air Force Research Laboratory, Air Vehicles Directorate
Principal Investigator and Technical Lead
1 September 2015 – 31 December 2015
Total award $22,000

Because of the widely varying flight conditions in which hypersonic vehicles operate and certain aspects unique to hypersonic flight, the development of control architectures for these vehicles presents a challenge. One particular safety concern in hypersonic flight is inlet unstarts, which not only produce a significant decrease in the thrust but also can lead to loss of control and possibly the loss of the vehicle. Previous work on control design for hypersonic vehicles often has involved linearized or simplified nonlinear dynamical models of the aircraft, but a better approach is a nonlinear adaptive dynamic inversion control architecture with a control allocation scheme. This approach was shown to handle time delays, perturbations in stability derivatives, and reduced control surface effectiveness while maintaining tracking performance. The technical objective of this effort is to extend the previous work and develop state-constraint enforcement methods for an adaptive nonlinear dynamic inversion architecture. State constraint enforcement methods are necessary for all classes of aircraft, but are important for hypersonic aircraft. When the engine is operating in what is called “dual-mode,” the isolator is susceptible to over-pressure due to combustion, which can result in an inlet unstart. At other points in the flight envelope, an inlet unstart can occur when certain angle-of-attack or sideslip limits are violated. Initial work into enforcement of envelope constraints has successfully been done in the context of an adaptive dynamic inversion control law that assumes full-state feedback.

TECHNICAL OBJECTIVES

Develop the formal theory for enforcement of both state and input constraints (position and rate limits) while maintaining stability.
Address an extension of state-constraint enforcement in an output-feedback adaptive dynamic inversion architecture. In this case, the state that must be constrained may not be directly measurable and therefore have to be estimated.

Working with me on this program are Research Assistants:

Douglas Famularo, Ph.D student
Sean Whitney, B.S. student

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

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.

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

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