Neural-network-based stochastic linear quadratic optimal tracking control scheme for unknown discrete-time systems using adaptive dynamic programming
Xin Chen
Fang Wang
School of Automation,China University of Geosciences,Wuhan 430074,Hubei,China;Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems,Wuhan 430074,Hubei,China
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) algorithm. First, 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 system. Next, 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 analysis. For 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, respectively. Finally, 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.
机标关键词：
论文发表日期：2021-08-05
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：13( 315-327 )
英文信息