Event-triggered tube model predictive control for high-speed trains trajectory tracking based on robust reachable set

The increasing speed and operation density have raised higher challenge to train trajectory tracking problem. In this paper, an event-triggered Tube Model Predictive Control (ETTMPC) controller is proposed, which can handle large frequent external disturbances with lower computational complexity. First, a classic MPC model of High-Speed Train (HST) trajectory tracking is established, then a Tube MPC (TMPC) model is introduced using the robust reachable set method, which improves computational time and space efficiency comparing to the traditional minimum robust positive invariant set (mRPI) method. In order to reduce the trigger frequency, an event-triggered strategy and the ETTMPC control structure are introduced, which only triggers computation when the trajectory deviation exceeds the threshold set. The simulation result shows that the proposed TMPC has higher robustness in large disturbance scenarios, while the ETTMPC has similar tracking performance as the TMPC but with a lower online computational cost.