Distributed Adaptive Coordinated Control for High-Speed Trains with Input Saturation Based on RBFNN and Sliding Mode Control

This paper addresses the distributed adaptive coordinated control for high-speed train (HST) fleet with uncertain parameters. The motion of the train in the fleet is constrained by its adjacent trains, necessitating dynamic adjustment mechanism facilitated through inter-train communication. For the uncertainty, radial basic function neural network (RBFNN) is introduced into the distributed adaptive coordinated control algorithm, which ensures behavioral consistency and short inter-train intervals for each train in the fleet. This paper compares the proposed method with distributed adaptive sliding mode control (DASMC). The simulation demonstrates better performance and benefits of this new algorithm. We show that the algorithm substantially reduces inter-train distance and ensures heightened level of behavioral consistency among all individual trains within the train fleet.