IEEE Trans Cybern. 2021 Dec;51(12):6294-6304. doi: 10.1109/TCYB.2020.2980048. Epub 2021 Dec 22.
This article investigates the design of self-triggered controllers for networked control systems (NCSs), where the dynamics of the plant are unknown a priori. To deal with the unknown transition dynamics, we employ the Gaussian process (GP) regression in order to learn the dynamics of the plant. To design the self-triggered controller, we formulate an optimal control problem, such that the optimal control and communication policies can be jointly designed based on the GP model of the plant. Moreover, we provide an overall implementation algorithm that jointly learns the dynamics of the plant and the self-triggered controller based on a reinforcement learning framework. Finally, a numerical simulation illustrates the effectiveness of the proposed approach.
本文研究了网络控制系统 (NCSs) 的自触发控制器设计问题，其中事先不知道植物的动态。为了处理未知的过渡动态，我们采用高斯过程 (GP) 回归来学习植物的动态。为了设计自触发控制器，我们制定了一个最优控制问题，以便能够基于植物的 GP 模型共同设计最优控制和通信策略。此外，我们提供了一种整体实现算法，该算法基于强化学习框架共同学习植物的动态和自触发控制器。最后，数值模拟说明了所提出方法的有效性。
