A novel linear matrix inequality-based robust event-triggered model predictive control for a class of discrete-time linear systems

Yingjie Hu, Ding Fan, Kai Peng, Herbert Ho Ching Iu, Xinan Zhang

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

This paper studies a linear matrix inequality (LMI)-based robust event-triggered model predictive control (ET-MPC) for a class of discrete linear time-invariant systems subject to bounded disturbances. In the presented robust event-triggered MPC, the event-triggered mechanism is first set by the deviation between the optimal state and actual state. In addition, the Lyapunov-based stability condition is employed in the constraints for simplifying parameters and enhancing the universality. Subsequently, a novel design framework that involves LMIs approach is developed. In such a framework, the Lyapunov weight matrix is predesigned by solving a convex optimization problem with LMIs and the infinite horizon MPC optimization problem is also transformed as LMIs such that the computational complexity is significantly reduced. To guarantee robust constraint satisfaction, a dual-mode control strategy is adopted. In this way, the proposed robust ET-MPC has the ability to deal with the constrained system. Furthermore, the theoretical analysis of the recursive feasibility and stability is provided. The numerical simulations and comparison studies demonstrate that the proposed robust ET-MPC not only has satisfying control performance but also significantly reduces the computational burden.

Original languageEnglish
Pages (from-to)4416-4435
Number of pages20
JournalInternational Journal of Robust and Nonlinear Control
Volume31
Issue number9
DOIs
Publication statusPublished - Jun 2021

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