Distributed Robust Model Predictive Control for High-Speed Train

This paper proposes a speed tracking control method for high-speed trains based on tube robust model predictive control (Tube-RMPC). The study aims to address critical challenges in train operation, including operational safety, disturbance rejection, and energy efficiency optimization. To achieve these objectives, the Mixed <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{H}_{2}/\mathrm{H}_{\infty}$</tex> control method and robust reachable set theory are integrated into the Tube-RMPC framework. This approach is intended to leverage the robust control capability, multi-objective optimization capability, and multi-constraint processing capability of Tube-RMPC. To address the challenge of excessive computation, a strategy for converting centralized MPC computation into distributed MPC computation is proposed. This approach reduces the matrix dimension in the optimization process, facilitating the implementation of the control method. The effectiveness of the proposed control method is validated through simulation experiments.