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.
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- John Valasek
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- Kameron Eves
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- Hannah Lehman
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/