李雅普诺夫函数
计算机科学
人工神经网络
控制理论(社会学)
最优控制
理论(学习稳定性)
数学优化
控制(管理)
人工智能
机器学习
数学
量子力学
物理
非线性系统
作者
Omkar S. Mulekar,Hancheol Cho,Riccardo Bevilacqua
出处
期刊:AIAA Journal
[American Institute of Aeronautics and Astronautics]
日期:2024-05-01
卷期号:62 (5): 1932-1945
摘要
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.
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