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

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

VSCL Student Presents at Interactive Learning with Implicit Human Feedback Workshop at 2023 International Conference on Machine Learning (ICML)

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VSCL graduate student M.D. Sunbeam will present a workshop paper on 29 July at the 2023 International Conference on Machine Learning (ICML) in Honolulu, Hawaii.

Sunbeam will be presenting the paper “Imitation Learning with Human Eye Gaze via Multi-Objective Prediction,”. Approaches for teaching learning agents via human demonstrations have been widely studied and successfully applied to multiple domains. However, the majority of imitation learning work utilizes only behavioral information from the demonstrator, i.e. which actions were taken, and ignores other useful information. In particular, eye gaze information can give valuable insight towards where the demonstrator is allocating visual attention, and holds the potential to improve agent performance and generalization. In this work, we propose Gaze Regularized Imitation Learning (GRIL), a novel context-aware, imitation learning architecture that learns concurrently from both human demonstrations and eye gaze to solve tasks where visual attention provides important context.

We apply GRIL to a visual navigation task, in which an unmanned quadrotor is trained to search for and navigate to a target vehicle in a photorealistic simulated environment. We show that GRIL outperforms several state-of-the-art gaze-based imitation learning algorithms, simultaneously learns to predict human visual attention, and generalizes to scenarios not present in the training data. Supplemental videos can be found at https://sites.google.com/view/gaze-regularized-il/, and code will be made available.

Two New Graduate Students Join VSCL in Fall 2023

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VSCL is proud to welcome two new graduate research assistants:

Jillian Bennett is a Masters of Science student in the Aerospace Engineering department. She graduated with her Bachelors of Science in Aerospace Engineering and Minor in Mathematics in Fall 2023. As an undergraduate she interned with Los Alamos National Laboratory and TAMU Material Science and Engineering, working on characterizing impacted materials. Additionally she was the lead ambassador for the Aerospace Ambassador program and a Fish Camp chair. Her work with VSCL will be primarily focused on Adaptive Control for Multiple Time Scale Systems.

 

 

 

2nd Lieutenant Noah Luna is a Masters of Science student in the Aerospace Engineering Department. He graduated from the United States Air Force Academy with a Bachelors of Science in Aeronautical Engineering and Computer Science. During his undergraduate studies, he performed research on a neural network based flight control system for an ongoing fixed-wing project through the Air Force Research Lab (AFRL). Additionally, he completed an internship and further research with Lockheed Martin Skunk Works as a Software and Flight Test engineer developing nonlinear adaptive flight controls for aerial systems. At VSCL, Noah will be working on Adaptive Control for Multiple Time Scale Systems.

VSCL graduate student Kameron Eves inducted into the AIAA Guidance, Navigation, and Control Technical Committee

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Kameron Eves, a recently graduated Ph.D. student in the Vehicle Systems & Control Laboratory (VSCL), has been inducted into the AIAA Intelligent Systems Technical Committee (GNCTC) for 2024.  The GNCTC is a group within AIAA that seeks to advance the technology and provide forums for the theoretical and practical consideration of techniques, devices and systems for the navigation, guidance and control of flight vehicles and the control of related aerospace systems.  Eves was inducted due to his technical experience in nonlinear control.   

Eves’s primary research topic is adaptive control for hypersonic systems in addition to reinforcement learning, autonomous control, and vehicle dynamics.  In the VSCL, Eves also worked to develop the capabilities necessary for autonomous reconnaissance in military settings. This project was a partnership with the National Robotics Engineering Center (NREC) at Carnegie Mellon University. Eves earned his bachelor’s degree in 2019 from Brigham Young University in Mechanical Engineering and joined the VSCL immediately after.  At BYU, Eves worked in the Multiple Agent Intelligent Coordination and Control (MAGICC) laboratory As part of this research, Kameron helped to develop a ground based optical tracking and imaging system capable of estimating an aircraft’s pose.

Jares and Valasek Publish “Control Acquisition Attack of Aerospace Systems via False Data Injection,” in Journal of Aerospace Information Systems

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Ph.D. student Garrett Jares and Dr. John Valasek of VSCL published the paper “Control Acquisition Attack of Aerospace Systems via False Data Injection,” in Journal of Aerospace Information Systems.

The cyber threat to aerospace systems has been growing rapidly in recent years with several real-world and experimental cyberattacks observed. This growing threat has prompted investigation of cyber-attack and defense strategies for manned and unmanned air systems, spacecraft, and other aerospace systems. The work in this paper seeks to further understand these attacks by introducing and developing a novel cyberattack for autonomous aerospace systems. The problem faced by the attacker is posed and discussed analytically using false data injection of state measurements to exploit the vehicle’s onboard controller to take control of the system. It is shown that the attacker can utilize traditional control techniques to exert control over the system and eliminate the control of the victim by intercepting and modifying the vehicle’s measurement data. The attacker is able to accomplish this objective without any prior knowledge of the system’s plant, controller, or reference signal. The attack is demonstrated on the elevator-to-pitch-attitude-angle dynamics of a Cessna T-37 aircraft model. It is shown to be successful in eliminating the victim’s control influence over the system and driving the system to its own target state.

This publication is part of VSCL’s ongoing work in the area of cybersecurity. The article can be viewed at https://arc.aiaa.org/doi/full/10.2514/1.I011199.