MPC-Based Speed Tracking Control for Subway Trains Under Actuator Constraints

In this paper, we propose a model predictive control (MPC)-based approach for the speed tracking control of subway trains, specifically designed to handle the complexities of constrained optimization in real-world applications. The control strategy accounts for nonlinearities, actuator constraints, and disturbances and is validated using real operational data from the Ningbo Metro Line 7. Unlike traditional PID controllers, which are limited by their inability to handle dynamic changes and nonlinear systems, the MPC method optimizes control inputs by predicting future system behavior and adapting to variations in speed profiles. The effectiveness of the proposed MPC-based controller is demonstrated through a comparison with the PID control system already deployed on the metro line. Our results show that MPC provides superior speed tracking performance, particularly in dynamic and disturbed operating conditions, highlighting its potential advantages for real-world subway train control systems.