Multiple High-Speed Trains Cooperative Tracking Control Based on Distributed Adaptive Model Predictive Control

This study focuses on cooperative tracking of multiple high speed trains, an effective way to accommodate rising passenger demand now that train to train communication is mature. A novel distributed adaptive model predictive control algorithm built on multi-point mass model is proposed. Firstly, the cooperative tracking task is recast with individual cars as the smallest nodes, yielding a distributed model predictive control strategy in which each car acts as a control agent. Subsequently, an adaptive weighting matrix is introduced to adjust the weights of multiple control input variables during the rolling optimization of model predictive control, thereby improving control accuracy and reducing tracking error. Furthermore, to accelerate computation, an adaptive prediction horizon scheme is presented that shortens or lengthens the horizon in real time according to the current tracking error. Finally, simulations with three high speed trains demonstrate that the proposed multi-point mass distributed adaptive model predictive control approach is both feasible and stable, highlighting its potential for enhancing cooperative tracking performance of multiple high-speed trains.