Optimal Control of Unknown Discrete-Time Linear Systems with Additive Noise  

作　　者：YANG Xue LIU Shujun 

机构地区：[1]School of Mathematics,Sichuan University,Chengdu 610064,China

出　　处：《Journal of Systems Science & Complexity》2023年第2期591-612,共22页系统科学与复杂性学报（英文版）

基　　金：supported by the National Natural Science Foundation of China under Grant No.61673284;the Science Development Project of Sichuan University under Grant No.2020SCUNL201。

摘　　要：The optimal control problem with a long run average cost is investigated for unknown linear discrete-time systems with additive noise.The authors propose a value iteration-based stochastic adaptive dynamic programming(VI-based SADP)algorithm,based on which the optimal controller is obtained.Different from the existing relevant work,the algorithm does not need to estimate the expectation(conditional expectation)and variance(conditional variance)of states or other relevant variables,and the convergence of the algorithm can be proved rigorously.A simulation example is given to verify the effectiveness of the proposed approach.

关 键 词：Discrete-time linear systems optimal control stochastic adaptive dynamic programming 

分 类 号：O232[理学—运筹学与控制论]