Tracking of unmanned hovercraft using event-based model predictive control with external disturbances

In this study, an event-based nonlinear model predictive control (EANMPC) method is developed to address the trajectory tracking problem of unmanned hovercraft subjected to unknown time-varying environmental disturbances and actuator saturation. Initially, the EANMPC method is applied to enhance tracking performance through rolling optimisation and online optimisation. Furthermore, it is difficult to transfer control signals to controlled systems, while the model predictive control (MPC) method occupies huge computational resources and makes it difficult to ensure timeliness. Moreover, the paper provides a proof of the adaptive control horizon’s stability and presents the simulation results of two cases. The results and tables indicate that the proposed method has better tracking performance and less computer resource cost.