An adaptive model predictive control strategy for a class of discrete‐time linear systems with parametric uncertainty. (4th October 2021)

• Record Type: Journal Article
• Authors: Hu, Yingjie; Gou, Linfeng; Fan, Ding; Iu, Herbert Ho‐Ching; Fernando, Tyrone; Zhang, Xinan
• Is Part Of: International journal of adaptive control and signal processing. Volume 35:Number 12(2021)
• Page Start: 2389
• Page End: 2405
• Publication Date: 2021-10-04
• Subjects: adaptive MPC -- closed‐loop stability -- constrained and unconstrained systems -- parameter estimation -- recursive feasibility; Adaptive control systems -- Periodicals; Adaptive signal processing -- Periodicals; 629.836
• DOI: 10.1002/acs.3327
• Languages: English
• ISSNs: 0890-6327

Abstract

Summary: In this study, an adaptive model predictive control (MPC) strategy is proposed for a class of discrete‐time linear systems with parametric uncertainty. In the presented adaptive MPC, an updating law is firstly designed to update the estimated parameters online. By utilizing the estimated parameters, a standard MPC optimization problem for unconstrained systems is established. Then, to deal with constrained systems, the min–max MPC technique is developed under the set‐based approach for the estimated parameters. Furthermore, it is shown theoretically that the recursive feasibility and closed‐loop stability can be rigorously proved, respectively. Finally, numerical simulations and comparative analysis are presented to illustrate the superiority of the proposed algorithms in control performance.