Adaptive MPC of a class of Switched Linear Systems with Unknown System Matrices

Hui Chen*, Licheng Sun, Hongbin Ma

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper presents an adaptive model predictive control for a class of switched linear stochastic systems with unknown system matrices. The proposed adaptive MPC is designed by solving a finite horizon constrained linear-quadratic optimal control problem of on-line estimated models, which are built on a switching recursive weighted least-squares (WLS) algorithm together with a random regularization method. By incorporating an attenuating excitation signal into adaptive MPC, the switched system satisfies the state constraints and control constraints after finite steps.

Original languageEnglish
Title of host publicationProceedings of the 43rd Chinese Control Conference, CCC 2024
EditorsJing Na, Jian Sun
PublisherIEEE Computer Society
Pages942-949
Number of pages8
ISBN (Electronic)9789887581581
DOIs
Publication statusPublished - 2024
Event43rd Chinese Control Conference, CCC 2024 - Kunming, China
Duration: 28 Jul 202431 Jul 2024

Publication series

NameChinese Control Conference, CCC
ISSN (Print)1934-1768
ISSN (Electronic)2161-2927

Conference

Conference43rd Chinese Control Conference, CCC 2024
Country/TerritoryChina
CityKunming
Period28/07/2431/07/24

Keywords

  • Adaptive Model Predictive Control
  • Switched Linear Systems
  • Weighted Least-Squares

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