Adaptive speed tracking control for high speed trains under stochastic operation environments

This paper investigates the adaptive speed tracking control problem of high-speed trains (HSTs) in the presence of stochastic disturbances. Considering the complicated operation environments, the HST dynamics are firstly formulated into a stochastic control system, and then an improved minimum variance self-tuning regulator is proposed by incorporating with an attenuation factor and the Tchebycheff series, which is proven to be able to improve the robustness of the HST system to the parameter estimation uncertainties and the stochastic disturbances. The global stability of the closed-loop system is rigorously analyzed by establishing the logarithm law of the improved generalized minimum variance loss function. Moreover, a sufficient condition for the optimality of the closed-loop system is provided, which also guarantees the convergency of the proposed control method. The effectiveness of the proposed control approach is demonstrated through numerical simulations.