Fusion event-triggered model predictive control based on shrinking prediction horizon

Purpose: This paper aims to design an algorithm which is used to deal with non-linear discrete systems with constraints under the lower computation burden. As a result, we solve the non-holonomic vehicle tracking problem with the lower computational load and the convergence performance. Design/methodology/approach: A fusion event-triggered model predictive control version is developed in this paper. The authors designed a shrinking prediction strategy. Findings: The fusion event-triggered model predictive control scheme combines the strong points of event triggered and self-triggered methods. As the practical state approaches the terminal set, the computational complexity of optimal control problem (OCP) decreases. Originality/value: The proposed strategy has proven to stabilize the system and also guarantee a reproducible solution for the OCP. Also, it is proved to be effected by the performance of the simulation results.