Stable Optimal Feedback Control for Landers Based on Machine Learning

Omkar S. Mulekar, Hancheol Cho, Riccardo Bevilacqua

Research output: Contribution to journalArticlepeer-review

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 languageEnglish
Pages (from-to)1932-1945
Number of pages14
JournalAIAA journal
Volume62
Issue number5
DOIs
Publication statusPublished - 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

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