A novel linear matrix inequality‐based robust event‐triggered model predictive control for a class of discrete‐time linear systems. (23rd March 2021)

• Record Type: Journal Article
• Authors: Hu, Yingjie; Fan, Ding; Peng, Kai; Iu, Herbert Ho‐Ching; Zhang, Xinan
• Other Names: Fridman Leonid guestEditor.; Efimov Denis guestEditor.; Laghrouche Salah guestEditor.
• Is Part Of: International journal of robust and nonlinear control. Volume 31:Number 9(2021)
• Page Start: 4416
• Page End: 4435
• Publication Date: 2021-03-23
• Subjects: discrete LTI system -- dual‐mode control -- linear matrix inequality -- Lyapunov‐based stability -- robust event‐triggered MPC; Automatic control -- Periodicals; Control theory -- Periodicals; Nonlinear systems -- Periodicals; 629.836
• DOI: 10.1002/rnc.5482
• Languages: English
• ISSNs: 1049-8923

Abstract

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.