Disturbance Prediction-Based Adaptive Event-Triggered Model Predictive Control for Perturbed Nonlinear Systems

A disturbance prediction-based adaptive event-triggered model predictive control scheme is proposed for nonlinear systems in the presence of slowly varying disturbance. The optimal control problem in the model predictive control scheme is formulated by taking advantage of a proposed central path-based disturbance prediction approach, and the event-triggered mechanism is designed to be adaptive to the triggering interval. As a result, the proposed scheme improves the state prediction precision and, hence, reduces greatly the triggering frequency. Furthermore, for input-affine nonlinear systems, the disturbance separation and compensation techniques are developed to further enlarge the triggering interval. The theoretical analysis of the algorithm feasibility and closed-loop stability, as well as numerical evaluations of the effectiveness of the proposed schemes, is also given.

Automated summary

This paper proposes an adaptive event-triggered model predictive control scheme based on disturbance prediction for nonlinear systems with slowly varying disturbance. The optimal control problem is formulated using a central path-based disturbance prediction approach, and the event-triggered mechanism is designed to adapt to the triggering interval. As a result, the proposed scheme improves state prediction precision and reduces triggering frequency. Additionally, disturbance separation and compensation techniques are developed for input-affine nonlinear systems to further extend the triggering interval. The theoretical analysis of algorithm feasibility, closed-loop stability, and numerical evaluations of the proposed schemes are also provided.