Data-Based Optimal Tracking of Autonomous Nonlinear Switching Systems

作     者：Xiaofeng Li Lu Dong Changyin Sun Xiaofeng Li;Lu Dong;Changyin Sun

作者机构：the School of AutomationSoutheast UniversityNanjing 210096 the Key Laboratory of Measurement and Control of Complex Systems of EngineeringMinistry of EducationSoutheast UniversityNanjing 210096China the College of Electronics and Information EngineeringTongji UniversityShanghai 201804China 

出 版 物：《IEEE/CAA Journal of Automatica Sinica》 (自动化学报（英文版）)

年 卷 期：2021年第8卷第1期

页      面：227-238页

核心收录：

学科分类：0810[工学-信息与通信工程] 1205[管理学-图书情报与档案管理] 07[理学] 070104[理学-应用数学] 0802[工学-机械工程] 0701[理学-数学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China(61921004 U1713209 61803085 and 62041301)。 

主　　题：Adaptive dynamic programming approximation error data-based control Q-learning switching system 

摘      要：In this paper,a data-based scheme is proposed to solve the optimal tracking problem of autonomous nonlinear switching systems.The system state is forced to track the reference signal by minimizing the performance function.First,the problem is transformed to solve the corresponding Bellman optimality equation in terms of the Q-function(also named as action value function).Then,an iterative algorithm based on adaptive dynamic programming(ADP)is developed to find the optimal solution which is totally based on sampled data.The linear-in-parameter(LIP)neural network is taken as the value function approximator.Considering the presence of approximation error at each iteration step,the generated approximated value function sequence is proved to be boundedness around the exact optimal solution under some verifiable assumptions.Moreover,the effect that the learning process will be terminated after a finite number of iterations is investigated in this paper.A sufficient condition for asymptotically stability of the tracking error is derived.Finally,the effectiveness of the algorithm is demonstrated with three simulation examples.