Abstract
Stability certification is critical before controllers are rolled out onto real systems. Despite recent progress in the development of neural network systems for feedback-optimal control, enforcement and assessment of the stability of the trained controllers remains an open problem. In this investigation, a comprehensive framework is developed to achieve certifiably stable fuel-optimal feedback control of pinpoint landers in four different formulations of varying complexity. By preconditioning a deep neural network policy and a deep neural network Lyapunov function, and then applying a constrained parameter optimization approach, we are able to address the shape mismatch problem posed by the standard sum-of-squares Lyapunov function and achieve feedback-optimal control. Phase-space plots of the Lyapunov derivative show the level of certificate enforcement achieved by the developed algorithms, and Monte Carlo simulations are performed to demonstrate the stable, optimal, real-time feedback control provided by the policy.
Original language | English |
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Pages (from-to) | 1932-1945 |
Number of pages | 14 |
Journal | AIAA journal |
Volume | 62 |
Issue number | 5 |
DOIs | |
Publication status | Published - 2024 May |
Bibliographical note
Publisher Copyright:© 2024 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.
All Science Journal Classification (ASJC) codes
- Aerospace Engineering