Q-Learing-Based Multi-Rate Optimal Control and Its Application
Zhenxing Xia, Wei Li Dai · 2022 34th Chinese Control and Decision Conference (CCDC) · 2022
In this paper, adaptive dynamic programming (ADP) algorithm, with lifting technology, is develop to solve the multi-rate optimal control problem for discrete-time linear systems. We make use of the lifting technology to convert the multi-rate sample control problem to the single-rate one in the uniform cycle. The propose a Q-Leaming based approach to learn the optimal regulator by a value iteration (VI) algorithm. First, a class of continuous-time (CT) linear system with multi-timescale is considered. Then, the convergence of a Q-Learning based algorithm is given. It is proven that the iterative cost function precisely converges to the optimal value, and the control input also converges to the optimal values. Finally, HIL system for grinding process is given to illustrate the effective performance of the proposed method.