Hybrid variables-dependent event-triggered model predictive control subject to polytopic uncertainties. Issue 14 (26th October 2022)

• Record Type: Journal Article
• Authors: Wan, Xiongbo; Wei, Fan; Zhang, Chuan-Ke; Wu, Min
• Is Part Of: International journal of systems science. Volume 53:Issue 14(2022)
• Page Start: 3042
• Page End: 3055
• Publication Date: 2022-10-26
• Subjects: Model predictive control -- dynamic event-triggered mechanism -- hybrid variables -- networked control systems -- polytopic uncertainties; System analysis -- Periodicals; 003.3
• DOI: 10.1080/00207721.2022.2068694
• Languages: English
• ISSNs: 0020-7721

Abstract

Abstract : This paper focuses on the model predictive control (MPC) issue where the measurable state is released under an event-triggered mechanism (ETM) to implement MPC over an infinite horizon. A new dynamic ETM (DETM) is devised to conserve network resources, which contains an additive internal dynamic variable (IDV), a multiplicative adaptively adjusting variable, a time-varying weighting matrix and several flexible scalars. The MPC problem is formulated as a 'min–max' optimisation problem (OP), where a hard constraint and robust positive invariant set on the predictive state/IDV are considered simultaneously. By resorting to a Lyapunov-like function that depends on the IDV, we put forward an auxiliary OP with matrix-inequality-based constraints. By the feasible solutions of such an auxiliary OP, the feedback gain matrix is designed which ensures the asymptotic stability of the closed-loop system. Two examples are presented to demonstrate the validity of the devised DETM and the DETM-based MPC algorithm. The study verifies that the devised DETM has advantages over an existing counterpart in conserving network resources while achieving the desired performance.