Event-based model predictive control for nonlinear systems with dynamic disturbance. (November 2022)

• Record Type: Journal Article
• Authors: Li, Pengfei; Wang, Tao; Kang, Yu; Li, Kun; Zhao, Yun-Bo
• Is Part Of: Automatica. Volume 145(2022)
• Publication Date: 2022-11
• Subjects: Model predictive control -- Event-based control -- Dynamic disturbance -- Disturbance prediction; Automatic control -- Periodicals; Automation -- Periodicals; 629.805
• DOI: 10.1016/j.automatica.2022.110533
• Languages: English
• ISSNs: 0005-1098

Abstract

Abstract: In this paper, we investigate the event-based model predictive control (MPC) for constrained nonlinear systems with dynamic disturbance. An event-triggered disturbance prediction MPC (DPMPC) scheme and a self-triggered counterpart, which explicitly consider the disturbance dynamics, are proposed. For the event-triggered DPMPC scheme, the triggering condition relying on the state prediction error and the predicted disturbance sequence, updates at each time step based on the system states. For the self-triggered DPMPC scheme, the next triggering instant is determined by using the optimal state sequence and predicted disturbance sequence. In both event-based schemes, the optimal control problems are solved only at triggering instants, thus reducing the consumption of computational resource. The effectiveness of the two schemes is demonstrated by a simulation example.