论文标题
安全控制，最小的遗憾
Safe Control with Minimal Regret
论文作者
论文摘要
随着我们朝着在非统计和不确定环境中运行的安全 - 关键的网络物理系统迈进时，缩小经典最佳控制算法和基于自适应学习的方法之间的差距至关重要。在本文中，我们提出了一种有效的基于优化的方法，用于计算有限的安全控制策略，从而使动态遗憾最小化，这是从损失的意义上讲，相对于千里眼控制器在事后选择中选择的最佳控制动作的最佳顺序。通过利用系统级别的合成框架（SLS），我们的方法将线性二次调节器的最小化结果扩展到最佳的遗憾，以受到硬安全性约束的最佳控制，并允许与安全感知的千里眼政策竞争。数值实验证实了在有限摩尼子限制的$ \ MATHCAL {H} _2 $和$ \ MATHCAL {H} _ \ INFTY $控制法律的卓越性能。
As we move towards safety-critical cyber-physical systems that operate in non-stationary and uncertain environments, it becomes crucial to close the gap between classical optimal control algorithms and adaptive learning-based methods. In this paper, we present an efficient optimization-based approach for computing a finite-horizon robustly safe control policy that minimizes dynamic regret, in the sense of the loss relative to the optimal sequence of control actions selected in hindsight by a clairvoyant controller. By leveraging the system level synthesis framework (SLS), our method extends recent results on regret minimization for the linear quadratic regulator to optimal control subject to hard safety constraints, and allows competing against a safety-aware clairvoyant policy with minor modifications. Numerical experiments confirm superior performance with respect to finite-horizon constrained $\mathcal{H}_2$ and $\mathcal{H}_\infty$ control laws when the disturbance realizations poorly fit classical assumptions.
