Neural-network-based stochastic linear quadratic optimal tracking control scheme for unknown discrete-time systems using adaptive dynamic programming

作     者：Xin Chen Fang Wang Xin Chen;Fang Wang

作者机构：School of AutomationChina University of GeosciencesWuhan430074HubeiChina Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex SystemsWuhan430074HubeiChina 

出 版 物：《Control Theory and Technology》 (控制理论与技术（英文版）)

年 卷 期：2021年第19卷第3期

页      面：315-327页

核心收录：

学科分类：08[工学] 0835[工学-软件工程] 081202[工学-计算机软件与理论] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work was supported by the National Natural Science Foundation of China(No.61873248) the Hubei Provincial Natural Science Foundation of China(Nos.2017CFA030,2015CFA010) the 111 project(No.B17040) 

主　　题：Stochastic system Optimal tracking control Adaptive dynamic programming Neural networks 

摘      要：In this paper,a stochastic linear quadratic optimal tracking scheme is proposed for unknown linear discrete-time(DT)systems based on adaptive dynamic programming(ADP)***,an augmented system composed of the original system and the command generator is constructed and then an augmented stochastic algebraic equation is derived based on the augmented ***,to obtain the optimal control strategy,the stochastic case is converted into the deterministic one by system transformation,and then an ADP algorithm is proposed with convergence *** the purpose of realizing the ADP algorithm,three back propagation neural networks including model network,critic network and action network are devised to guarantee unknown system model,optimal value function and optimal control strategy,***,the obtained optimal control strategy is applied to the original stochastic system,and two simulations are provided to demonstrate the effectiveness of the proposed algorithm.