Event-Triggered Distributed MPC With Variable Prediction Horizon
Aoyun Ma, Kun Liu, Qirui Zhang, Tao Liu, Yuanqing Xia
Abstract
This article presents an event-triggered distributed model predictive control with variable prediction horizon strategy for spatially interconnected systems with input and state constraints. Each subsystem compares the error between actual state and optimal state with a triggering level, and cooperates with other subsystems to determine the triggering time and the corresponding prediction horizon. A shrinking constraint related to the bounds of errors between the predicted states and the optimal states of the previous triggering time instant is introduced into the optimization problem. By implementing the proposed strategy, the number of optimization problems that need to be solved is decreased, and the complexity of the optimization problem is reduced with the actual state approaching the terminal set. The feasibility of the optimization problem and the asymptotic stability of the closed-loop system are established. Finally, the simulation results show that the proposed strategy works well.