A Data-Based Feedback Relearning Algorithm for Uncertain Nonlinear Systems  

作　　者：Chaoxu Mu Yong Zhang Guangbin Cai Ruijun Liu Changyin Sun 

机构地区：[1]the School of Electrical and Information Engineering,Tianjin University,Tianjin 300072,China [2]the College of Missile Engineering,Rocket Force University of Engineering,Xi’an 710025,China [3]the School of Computer and Information Engineering,Beijing Technology and Business University,Beijing 100048,China [4]the School of Automation,Southeast University,Nanjing 210096,China

出　　处：《IEEE/CAA Journal of Automatica Sinica》2023年第5期1288-1303,共16页自动化学报（英文版）

基　　金：supported in part by the National Key Research and Development Program of China(2021YFB1714700);the National Natural Science Foundation of China(62022061,6192100028)。

摘　　要：In this paper,a data-based feedback relearning algorithm is proposed for the robust control problem of uncertain nonlinear systems.Motivated by the classical on-policy and off-policy algorithms of reinforcement learning,the online feedback relearning(FR)algorithm is developed where the collected data includes the influence of disturbance signals.The FR algorithm has better adaptability to environmental changes(such as the control channel disturbances)compared with the off-policy algorithm,and has higher computational efficiency and better convergence performance compared with the on-policy algorithm.Data processing based on experience replay technology is used for great data efficiency and convergence stability.Simulation experiments are presented to illustrate convergence stability,optimality and algorithmic performance of FR algorithm by comparison.

关 键 词：Data episodes experience replay neural networks reinforcement learning(RL) uncertain systems 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] TP13[自动化与计算机技术—控制科学与工程]