文献类型：期刊文献

英文题名：Data-driven policy iteration algorithm for optimal control of continuous-time Ito stochastic systems with Markovian jumps

作者：Song, Jun[1];He, Shuping[2];Liu, Fei[3];Niu, Yugang[1];Ding, Zhengtao[4]

机构：[1]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Anhui Univ, Sch Elect Engn & Automat, Hefei 230601, Peoples R China;[3]Jiangnan Univ, Inst Automat, Key Lab Adv Proc Control Light Ind, Minist Educ, Wuxi 214122, Peoples R China;[4]Univ Manchester, Sch Elect & Elect Engn, Control Syst Ctr, Sackville St Bldg, Manchester M13 9PL, Lancs, England

年份：2016

卷号：10

期号：12

起止页码：1431

外文期刊名：IET CONTROL THEORY AND APPLICATIONS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000381410000014)】；

基金：This work was supported in part by the NNSF from China (61203051, 61273073), and the Foundation for Distinguished Young Scholars of Anhui Province (1608085J05).

语种：英文

外文关键词：stochastic systems; continuous time systems; iterative methods; Markov processes; convergence of numerical methods; Riccati equations; transforms; optimal control; ST-based data-driven policy iteration algorithm; infinite horizon optimal control problem; continuous-time Ito stochastic systems; Markovian jumps; multiplicative noises; stochastic coupled algebraic Riccatic equation; stochastic CARE; offline iteration algorithm; implicit iterative algorithm; subsystems transformation technique; parallel Kleinman iterative equations

摘要：This studies the infinite horizon optimal control problem for a class of continuous-time systems subjected to multiplicative noises and Markovian jumps by using a data-driven policy iteration algorithm. The optimal control problem is equivalent to solve a stochastic coupled algebraic Riccatic equation (CARE). An off-line iteration algorithm is first established to converge the solutions of the stochastic CARE, which is generalised from an implicit iterative algorithm. By applying subsystems transformation (ST) technique, the off-line iterative algorithm is decoupled into N parallel Kleinman's iterative equations. To learn the solution of the stochastic CARE from N decomposed linear subsystems data, an ST-based data-driven policy iteration algorithm is proposed and the convergence is proved. Finally, a numerical example is given to illustrate the effectiveness and applicability of the proposed two iterative algorithms.