Robust efficient cruise control for high-speed train movement based on the self-triggered mechanism

This paper investigates the robust efficient self-triggered cruise control problem for high-speed trains to reduce the consumptions of the computational resources and wireless communication bandwidth. By capturing the dynamic evolution of high-speed trains in the actual environment, an error dynamic state-space model is established considering uncertain parameters, input constraints and disturbances to the train motion. According to the event-triggered control framework, a co-design method is introduced to design the cruise controller and the event-triggered condition jointly. Based on the Lyapunov stability method, a sufficient condition for the existence of the robust state-feedback control law and the event-triggered condition parameter are presented in terms of linear matrix inequalities (LMIs), which ensures that the high-speed train tracks the desired speed, and the coupler deviations are stable at the equilibrium point under external disturbances. With the fact that the event-triggered controller often requires a dedicated detector to continuously monitor the system state, a self-triggered implementation is further developed to dynamically calculate the next triggered instant at each update time based on recently collected information. In the end, numerical experiments are provided to illustrate the effectiveness of the proposed approach in tracking the desired movement profiles, and thereby demonstrates the advantages of the proposed self-triggered controller over the traditional periodic triggered control method.