Robust Nonlinear MPC With Variable Prediction Horizon: An Adaptive Event-Triggered Approach

This article investigates the event-triggered model predictive control (ETMPC) problem for nonlinear systems with the bounded disturbance. First, a novel adaptive event-triggered mechanism without Zeno behaviors, in which the triggering threshold can constantly be adjusted with the change of the system state, is proposed for computational load reduction. Then, an adaptive prediction horizon update strategy is proposed to further reduce the computational complexity of the optimization problem at each triggering instant. Moreover, a dual-mode ETMPC algorithm is developed, and sufficient conditions on the algorithm feasibility and the system robust stability are provided. Through a simulation example, the results show that the proposed scheme can use fewer computational resources and a shorter calculation time for solving the optimization problem while ensuring satisfactory system performances than the existing ones.