Convergence of adaptive MPC for linear stochastic systems  

作　　者：Hui CHEN Lei GUO 

机构地区：[1]Institute of Systems Science,Academy of Mathematics and Systems Science(AMSS),Chinese Academy of Sciences,Beijing 100190,China

出　　处：《Science China(Information Sciences)》2023年第5期126-142,共17页中国科学（信息科学）（英文版）

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

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.

关 键 词：adaptive control linear-quadratic optimal control model predictive control uncertain stochastic system weighted least-squares 

分 类 号：TP13[自动化与计算机技术—控制理论与控制工程]