Speaker: 陈辉
Title: Feasibility and Convergence of Adaptive MPC
Language: Chinese
Time & Venue: 2024.04.20 10:30-12:00 思源楼415
Abstract: The main content of this talk is to design adaptive model predictive control (MPC) algorithms for linear stochastic systems with parameter uncertainties, and to establish the feasibility and convergence of the adaptive MPC in theory. For unconstrained discrete-time linear uncertain stochastic systems, we establish the convergence of an adaptive MPC. Under some basic assumptions, we establish the stability of the closed-loop system and the asymptotic convergence of the long-run average performance by using the weighted least-square algorithm and the random regularization method. Furthermore, we also establish both the feasibility of the adaptive MPC and the convergence of the closed-loop control performance for a class of constrained discrete-time linear uncertain stochastic systems. Finally, we design and analyze an adaptive stochastic MPC for a class of discrete-time linear systems with chance constraints.