论文标题
通过安全指数合成和凸半无限编程持续可行的可行的强大安全控制
Persistently Feasible Robust Safe Control by Safety Index Synthesis and Convex Semi-Infinite Programming
论文作者
论文摘要
模型不匹配在现实世界中占上风。确保具有不确定动态模型的系统的安全至关重要。但是，当存在控制限制时，现有的强大安全控制器可能无法实现。现有方法使用不确定性的过度评估，从而导致保守的安全控制。为了应对这些挑战，我们为有限状态依赖性的不确定性提出了一个控制限制的稳健安全控制框架。我们建议安全指数综合，以找到一个可确保在控制限制下可实现的可靠安全控制器。而且，我们通过凸半无限编程来解决可靠的安全控制，这是凸有界不确定性的最紧密配方，并导致最不保守的控制。此外，我们分析了在未建模的不确定性下可以保留安全性的何时以及如何保留安全性。实验结果表明，在控制限制下，我们的稳健安全控制器始终是可实现的，并且不如强基地保守得多。
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.
