Convergence of adaptive MPC for linear stochastic systems

作     者：Hui CHEN Lei GUO 

作者机构：Institute of Systems Science Academy of Mathematics and Systems Science (AMSS) Chinese Academy of Sciences 

出 版 物：《Science China(Information Sciences)》 (中国科学:信息科学(英文版))

年 卷 期：2023年第66卷第5期

页      面：130-146页

核心收录：

学科分类：0711[理学-系统科学] 07[理学] 08[工学] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 071102[理学-系统分析与集成] 081103[工学-系统工程] 

基　　金：supported by National Natural Science Foundation of China (Grant No. 12288201) National Center for Mathematics and Interdisciplinary Sciences,CAS 

主　　题：adaptive control linear-quadratic optimal control model predictive control uncertain stochastic system weighted least-squares 

摘      要：The convergence of an adaptive model predictive control(MPC) algorithm for discrete-time linear stochastic systems with unknown parameters is investigated in this paper. The proposed adaptive MPC is designed by solving a finite horizon constrained linear-quadratic optimal control problem of online estimated models, which are built on a recursive weighted least-squares(WLS) algorithm together with a random regularization method. By incorporating an attenuating excitation signal into adaptive MPC, the proposed adaptive MPC is shown to converge asymptotically to the ergodic MPC performance with known parameters by using the Markov chain ergodic theory.