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

Cassie-Kay McQuinn Graduates with Masters

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Cassie-Kay McQuinn graduated with her MS degree in aerospace engineering. Cassie is the 60th graduate student advised to completion of their degree by Dr. Valasek, and the title of her thesis is “Online Near-Real Time Open-Loop System Identification from Closed-Loop Flight Test Data”. This work is sponsored by the National Science Foundation (NSF) Center for Autonomous Air Mobility & Sensing (CAAMS) as one part of the project “Integration of System Theory with Machine Learning Tools for Data Driven System Identification”. Cassie investigated identifying state-space linear dynamic models generated onboard in near-real time, for vehicles with and without an active flight controller.

Cassie is continuing on to the PhD with VSCL, and her dissertation will be based upon work she has been conducting on STARS (Safe Trusted Autonomy for Responsible Spacecraft) during a year-round internship for the Air Force Research Laboratory.

        

VSCL Presents System Identification Project Update at Center for Autonomous Air Mobility and Sensing (CAAMS) Summer Meeting

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In conjunction with Dr. Moble Benedict (AVFL Lab – TAMU), Dr Puneet Singla (CASS Lab – Penn State), and Dr. Randy Beard (MAGICC Lab – BYU), Dr. Valasek presented the current updates of the System Identification project at the National Science Foundation (NSF) CAAMS Summer Industry Advisory Board Meeting.

The project “Integration of System Theory with Machine Learning Tools for Data Driven System Identification” 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.

The center is a partnership between academia, industry, and government to offer pre-competitive research in autonomous air mobility and sensing. Pictured (left to right) are Undergraduate Researcher Halle Vandersloot, PhD student Cassie-Kay McQuinn, Dr. Valasek, and TAMU AERO alum and VP of Engineering of VectorNav Dr. Jeremy Davis.

Valasek Receives Teaching Impact Award and Engineering Genesis for Multidisciplinary Research

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Dr. John Valasek received two distinguishing awards during the Spring 2024 semester. In the Faculty Excellence Awards category, Valasek was awarded the College of Engineering Teaching Impact Award. This award recognizes individuals who have had a profound impact on students through their teaching. Valasek received the award for the career achievements of his former graduate and undergraduate students.

Valasek was also awarded the Engineering Genesis Award for Multidisciplinary Research. This award was created to honor Texas A&M Engineering Experiment Station (TEES) researchers who have secured a grant of $1 million or more for a research project. Valasek is the PI for the project: “Enhancing the Cycle-of-Learning for Autonomous Systems to Facilitate Human-Agent Teaming”.

VSCL Hosts Entrepreneur Chen

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VSCL hosts Mr. Clay Chen, an entrepreneur interested in UAS systems. Mr. Chen met with VSCL lab director Dr. Valasek and VSCL graduate and undergraduate students  to discuss the UAS research and platform developments that VSCL conducts at the flight testing facility.

MD Sunbeam Defends Masters Thesis

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MD Sunbeam (B.S. Aerospace Engineering, University of Texas) successfully defended his Masters thesis: “Gaze-Regularized Imitation Learning”. 

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 photo-realistic 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.

This work is sponsored by the Army Research Laboratory (ARL) through the Cycle of Learning Project. MD Sunbeam is employed as a researcher at the Human Research and Engineering Directorate (HRED), ARL.

Bennett Awarded the Aerospace Engineering Graduate Excellence Fellowship

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For the Fall of 2025, Jillian Bennett received the Aerospace Engineering Graduate Excellence Fellowship, a competitive fellowship selected by the AERO Graduate Program Committee with an award of $1,000.

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, working on flight testing for the System Identification project and extending KAMS as part of her thesis work. Jillian has an interest in flight testing, nonlinear control, and vehicle dynamics.

VSCL Students Graduate with B.S. Degrees

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Congratulations to the VSCL undergraduate research assistants who graduated with a Bachelor of Science in Aerospace Engineering from Texas A&M University on May 10th 2024!

McQuinn Awarded the J. Malon Southerland ’65 Leadership Scholarship

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VSCL graduate student Cassie-Kay McQuinn is the recipient of the J. Malon Southerland Aggie Leader Scholarship. The J. Malon Southerland Aggie Leader Scholarship program was created to recognize and reward students involvement at Texas A&M University. The scholarship was named in honor of J. Malon Southerland, former TAMU Vice President for Student Affairs. While a student at Texas A&M Cassie-Kay has been involved in leadership through membership of the Student Engineers’ Council (SEC), completion of the Zachry Leadership Program (ZLP), and has been Vice President then President of the Texas A&M chapter of Sigma Gamma Tau (SGT) the National Honor Society for Aerospace Engineering.

Cassie-Kay McQuinn Defends Masters Thesis

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Cassie-Kay McQuinn (B.S. Aerospace Engineering, TAMU) successfully defended her Masters thesis titled “Online Near-Real Time Open-Loop System Identification from Closed-Loop Flight Test Data“.

Cassie-Kay’s thesis investigated identifying linear dynamic models onboard a vehicle in near-real time with and without an active controller. This is performed for a small Unmanned Air System (UAS) utilizing low cost, commercial-off-the-shelf components. Bare airframe longitudinal, lateral/directional and combined longitudinal lateral/directional models of the test vehicle are generated both onboard the vehicle during flight and offline during post-processing. The Developmental Flight Test Instrumentation 2 (DFTI2), utilizing the Robot Operating System (ROS), is extended to compute system models onboard the vehicle from both open-loop and closed-loop data. Additionally, a controller is implemented into the system, external to the primary flight controller, to generate and record controller inputs for the closed-loop system. The Observer/Kalman filter Identification (OKID) algorithm is used to generate locally linear models of the flight vehicle. Models are generated independent of actuator dynamics by mapping deflection angle to measured servo potentiometer readings. Orthogonal Schroeder sine sweep excitations are utilized to reduce potential control coupling while also exciting multiple frequencies. Identified models are presented and evaluated. Offline analysis of closed-loop flight data provides insight into the controller utilized in flight. Results presented in the thesis show the extended system can generate models suitable for describing the dynamics of the vehicle operating both with and without a controller implemented.

This work is sponsored by the National Science Foundation (NSF)  Center for Autonomous Air Mobility and Sensing (CAAMS). Conference and journal papers are being written on this work. Cassie-Kay’s is the 60th graduate degree earned by a VSCL graduate student.

Krpec and Valasek Publish “Vision-based Marker-less Landing of a UAS on Moving Ground Vehicle” in Journal of Aerospace Information Systems

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VSCL Alumni Blake Krpec, Dr. John Valasek, and Dr. Stephen Nogar with the DEVCOM Army Research Lab published the paper “Vision-based Marker-less Landing of a UAS on Moving Ground Vehicle” in Journal of Aerospace Information Systems.

Current autonomous unmanned aerial systems (UAS) commonly use vision-based landing solutions that depend upon fiducial markers to localize a static or mobile landing target relative to the UAS. This paper develops and demonstrates an alternative method to fiducial markers with a combination of neural network-based object detection and camera intrinsic properties to localize an unmanned ground vehicle (UGV) and enable autonomous landing. Implementing this visual approach is challenging given the limited compute power on board the UAS, but is relevant for autonomous landings on targets for which affixing a fiducial marker a priori is not possible, or not practical. The position estimate of the UGV is used to formulate a landing trajectory that is then input to the flight controller. Algorithms are tailored towards low size, weight, and power constraints as all compute and sensing components weigh less than 100 g. Landings were successfully demonstrated in both simulation and experimentally on a UGV traveling in both a straight line and while turning. Simulation landings were successful at UGV speeds of up to 3.0 m/s, and experimental landings at speeds up to 1.0 m/s.