Self-Triggered Model Predictive Control for Perturbed Nonlinear Systems: An Iterative Implementation

In this paper, a novel iterative self-triggered model predictive control strategy is proposed for continuous-time nonlinear systems with external disturbances. For this strategy, the triggering instants are determined by iteratively using the self-triggered mechanism. To be specific, the triggering mechanism, on the one hand, determines the next sampling instants of the sensor by a prespecified condition, and, on the other hand, decides whether or not to treat the current sampling instant as the triggering instant. Without continuous monitoring of the state, the sensing cost of the sensor can be alleviated. The utilization of the sampling states after the triggering instant leads to a larger triggering interval, and the computational load of the controller can thus be reduced. The effectiveness of the proposed strategy is validated by a numerical example.