Adaptive Optimal Output Regulation of Interconnected Singularly Perturbed Systems With Application to Power Systems  

作　　者：Jianguo Zhao Chunyu Yang Weinan Gao Linna Zhou Xiaomin Liu 

机构地区：[1]the Engineering Research Center of Intelligent Control for Underground Space,Ministry of Education,China University of Mining and Technology,Xuzhou 221116 [2]the School of Information and Control Engineering,China University of Mining and Technology,Xuzhou 221116 [3]IEEE [4]the State Key Laboratory of Synthetical Automation for Process Industries,Northeastern University,Shenyang 110819,China

出　　处：《IEEE/CAA Journal of Automatica Sinica》2024年第3期595-607,共13页自动化学报（英文版）

基　　金：supported by the National Natural Science Foundation of China (62073327,62273350);the Natural Science Foundation of Jiangsu Province (BK20221112)。

摘　　要：This article studies the adaptive optimal output regulation problem for a class of interconnected singularly perturbed systems(SPSs) with unknown dynamics based on reinforcement learning(RL).Taking into account the slow and fast characteristics among system states,the interconnected SPS is decomposed into the slow time-scale dynamics and the fast timescale dynamics through singular perturbation theory.For the fast time-scale dynamics with interconnections,we devise a decentralized optimal control strategy by selecting appropriate weight matrices in the cost function.For the slow time-scale dynamics with unknown system parameters,an off-policy RL algorithm with convergence guarantee is given to learn the optimal control strategy in terms of measurement data.By combining the slow and fast controllers,we establish the composite decentralized adaptive optimal output regulator,and rigorously analyze the stability and optimality of the closed-loop system.The proposed decomposition design not only bypasses the numerical stiffness but also alleviates the high-dimensionality.The efficacy of the proposed methodology is validated by a load-frequency control application of a two-area power system.

关 键 词：Adaptive optimal control decentralized control output regulation reinforcement learning(RL) singularly perturbed systems(SPSs) 

分 类 号：TM73[电气工程—电力系统及自动化] TP273[自动化与计算机技术—检测技术与自动化装置]