An error gradient and accumulation-type event-driven model predictive control with relative thresholds for perturbed nonlinear systems

This work investigates the event-driven model predictive control problem for perturbed nonlinear systems with input and state constraints. Firstly, a new event-driven triggering condition, considering the gradient and accumulation of the error between the optimal and actual states at adjacent moments with a state-dependent relative threshold is designed with a guaranteed positive inter-event time to avoid Zeno behaviour. Secondly, an error gradient and accumulation type event-driven model predictive control framework is designed based on the new triggering condition and the dual-mode strategy, for effectively reducing the calculation and communication burden of the model predictive control controller while ensuring desired control performance. Furthermore, the feasibility and the input-to-state practical stability (ISpS) of gradient and accumulation-type event-driven model predictive control are rigorously guaranteed theoretically. At last, the simulation results are provided to validate the this algorithm.