Event-Triggered Adaptive Horizon Model Predictive Control for Perturbed Nonlinear Systems

This paper proposes a new event-triggered adaptive horizon model predictive control for discrete-time nonlinear systems with additive disturbance. With the event-triggered control scheme, the optimization problem is solved only at triggering instant and the event is triggered if the difference between the actual state and the predicted state exceeds the triggering threshold. The triggering threshold depends on the prediction horizon and becomes larger as the state approaches the terminal constraint set. Therefore, larger triggering intervals can then be obtained. Finally, a numerical example shows the effectiveness of the proposed scheme.