Research on Feed Forward Adaptive-generalized Model Predictive Control for High-speed Train Automatic Operation

Abstract

High-speed automatic train operation (ATO) systems inherently exhibit strong nonlinearity and uncertainty. In view of the characteristics of nonlinearity and time-variability of high-speed train model parameters, this paper proposes a feed forward adaptive-generalized model predictive control (FA-GPC) method for dynamic optimization control of the ATO system along with a constrained multi-object predictive controller. Based on a multi-particle train model, an initial analysis explores the impacts of additional resistance changes on train operation. Subsequently, a multi-object performance indicator function containing control input constraints is constructed, combined with key indicators during the operation of high-speed trains, such as speed tracking accuracy, stopping accuracy, and riding comfort. Furthermore, a feed forward generalized prediction speed tracking control algorithm is designed based on the multi-object function, aiming to solve controller overshoot due to additional resistance changes and enhance control convergence rates. Taking into account various factors including influences of external environments and passenger movement during train operation, the resistance changes greatly, making it difficult to establish an accurate mathematical model, a constrained variable forgetting factor-recursive least square method is incorporated to identify the controlled auto-regressive integrated moving average model (CARIMA) of the train control system under different operational conditions. This approach aims to improve the robustness of the control system. Simulation results show that, compared with traditional GPC in the absence of the feed forward functionality and PID controller, the proposed feed forward generalized controller demonstrates good cruise control speed tracking accuracy within a ±0.5 km/h range under different line conditions and strong robustness thanks to the adaptive modification to the feed forward generalized predictive control algorithm for better performance in strong disturbance conditions.

Keywords

• high-speed train
• automatic operation
• adaptive-generalized model predictive control
• parameter identification