MPC-PI cascade control method for heavy-haul train

Abstract

With the development of ATO (Automatic Train Operation) system for heavy-haul trains, advanced control methods to ensure the safe and stable operation of the train have become an urgent demand. To this end, a cascade control method based on MPC-PI (model predictive control and proportional integral) control is proposed. A multi-particle dynamic model of heavy-haul trains is established, and then the model is linearized for control algorithm design. In this cascade control method, the outer loop adopts MPC with large sampling time, and the optimization objectives consist of in-train forces, energy consumption and speed tracking error. The MPC control is formulated as QP (Quadratic Programming) problem, the output of which is used as the initial input of the inner loop. The inner loop adopts PI control with small sampling time, and adjusts the actual train control output in real time for the speed tracking error. The simulation results of 20,000 tons heavy-haul train show that the proposed method has better robustness and anti-interference than MPC and PI control separately. The optimization objective function is formulated with weighted coupler force and energy consumption, and the simulation results are compared with different sets of weights, which prove the effectiveness of the proposed method.