论文标题
使用参考调查员对线性系统的受限控制的在线凸优化
Online convex optimization for constrained control of linear systems using a reference governor
论文作者
论文摘要
在这项工作中，我们为线性系统提出了一个控制方案，以在时间状态下屈服，并旨在最大程度地减少时间变化和先验不明的成本函数。拟议的控制器基于在线凸优化和参考调查员。特别是，我们应用在线梯度下降来跟踪系统变化和先验的最佳稳态。此外，我们使用$λ$ - 扣取设置来实施约束满意度，并将闭环系统的足够收敛速率与最佳稳态相关。我们证明所提出的方案是可行的，确保始终满足状态和输入约束，并实现动态遗憾，该遗憾是由成本函数的变化线性界定的。通过模拟示例说明了算法的性能和约束满意度。
In this work, we propose a control scheme for linear systems subject to pointwise in time state and input constraints that aims to minimize time-varying and a priori unknown cost functions. The proposed controller is based on online convex optimization and a reference governor. In particular, we apply online gradient descent to track the time-varying and a priori unknown optimal steady state of the system. Moreover, we use a $λ$-contractive set to enforce constraint satisfaction and a sufficient convergence rate of the closed-loop system to the optimal steady state. We prove that the proposed scheme is recursively feasible, ensures that the state and input constraints are satisfied at all times, and achieves a dynamic regret that is linearly bounded by the variation of the cost functions. The algorithm's performance and constraint satisfaction is illustrated by means of a simulation example.
