Adaptive composite anti-disturbance control for heavy haul trains

Abstract

In this paper, an adaptive composite anti-disturbance control of heavy haul trains (HHTs) is proposed. First, the mechanical principle and characteristics of couplers are analyzed and the longitudinal multi-particles nonlinear dynamic model of HHTs is established, which can satisfy that the forces of vehicles in different positions are different. Subsequently, a radial basis function network (RBFNN) is employed to approximate the uncertainties of HHTs, and a nonlinear disturbance observer (NDO) is constructed to estimate the approximation error and external disturbances. To indicate and improve the approximation accuracy, a serial-parallel identification model of HHTs is constructed to generate a prediction error, and an adaptive composite anti-disturbance control scheme is developed, where the prediction error and tracking error are employed to update RBFNN weights and an auxiliary variable of NDO. Finally, the feasibility and effectiveness of proposed control scheme are demonstrated through the Lyapunov theory and simulation experiments.