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.

Control Theory

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)

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

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

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