安全指数合成と凸半無限計画による持続的に実行可能なロバスト安全制御
Persistently Feasible Robust Safe Control by Safety Index Synthesis and Convex Semi-Infinite Programming
制御限界下でも実行可能なロバスト安全制御器を安全指数合成で設計し、凸半無限計画で保守性を抑えて解くフレームワークを提案した。
著者: Tianhao Wei, Shucheng Kang, Weiye Zhao, Changliu Liu
分類: cs.RO
原文アブストラクト
Model mismatches prevail in real-world applications. Ensuring safety for systems with uncertain dynamic models is critical. However, existing robust safe controllers may not be realizable when control limits exist. And existing methods use loose over-approximation of uncertainties, leading to conservative safe controls. To address these challenges, we propose a control-limits aware robust safe control framework for bounded state-dependent uncertainties. We propose safety index synthesis to find a robust safe controller guaranteed to be realizable under control limits. And we solve for robust safe control via Convex Semi-Infinite Programming, which is the tightest formulation for convex bounded uncertainties and leads to the least conservative control. In addition, we analyze when and how safety can be preserved under unmodeled uncertainties. Experiment results show that our robust safe controller is always realizable under control limits and is much less conservative than strong baselines.
