Robust Event-Triggered Model Predictive Control for Multiple High-Speed Trains With Switching Topologies

Abstract

This paper presents a robust event-triggered model predictive control (MPC) strategy for multiple high-speed trains (MHSTs) with random switching topologies. Due to the complicated operation environment of high-speed railways, the communication topology of MHSTs system is time-varying and changes among a set of directed graphs, which can be characterized as a Markov chain. By adopting the concept of MPC, this paper studies the distributed cooperative leader-following consensus control for MHSTs, in which a novel event-triggered strategy is introduced to determine when information exchange among neighboring trains and control update are executed. Firstly, the leader-following consensus problem of MHSTs system is transformed to the stabilization of a Markov jump system and a sufficient condition for leader-following consensus is derived with stability analysis of the Markov jump system based on the robust event-triggered MPC scheme. Then, the robust event-triggered MPC algorithm which minimizes the objective function is proposed. By optimizing the objective function, the deviation of states and amplitude of the control force are optimized. The effectiveness of the proposed robust event-triggered MPC method on cooperative cruise control of MHSTs is illustrated by numerical examples.