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 couplers are analyzed longitudinal multi-particles nonlinear dynamic model HHTs established, which can satisfy that forces vehicles in different positions different. Subsequently, a radial basis function network (RBFNN) employed to approximate uncertainties HHTs, disturbance observer (NDO) constructed estimate approximation error external disturbances. To indicate improve accuracy, serial-parallel identification generate prediction error, scheme developed, where tracking update RBFNN weights auxiliary variable NDO. Finally, feasibility effectiveness proposed demonstrated through Lyapunov theory simulation experiments.