ロバストなニューラルLyapunov-Barrier関数による安全な非線形制御
Safe Nonlinear Control Using Robust Neural Lyapunov-Barrier Functions
モデル不確かさに頑健な制御Lyapunov-Barrier関数を学習し、安全性と安定性を保証するフィードバック制御器を合成する手法を提案。ロバストMPCと同等以上の性能を1桁低い計算コストで実現した。
著者: Charles Dawson, Zengyi Qin, Sicun Gao, Chuchu Fan
分類: eess.SY, cs.RO, cs.SY
原文アブストラクト
Safety and stability are common requirements for robotic control systems; however, designing safe, stable controllers remains difficult for nonlinear and uncertain models. We develop a model-based learning approach to synthesize robust feedback controllers with safety and stability guarantees. We take inspiration from robust convex optimization and Lyapunov theory to define robust control Lyapunov barrier functions that generalize despite model uncertainty. We demonstrate our approach in simulation on problems including car trajectory tracking, nonlinear control with obstacle avoidance, satellite rendezvous with safety constraints, and flight control with a learned ground effect model. Simulation results show that our approach yields controllers that match or exceed the capabilities of robust MPC while reducing computational costs by an order of magnitude.
