A neural network-based adaptive fault-tolerant cooperation control for multiple trains with unknown parameters

For the problems of parametric uncertainties and actuator faults encountered during high-speed train operation, this paper proposes a neural network-based adaptive fault-tolerant control strategy for cooperation of multiple trains. First, a multi-train model with state constraints, disturbances, and actuator faults was established, and the radial basis function neural network was utilized to fit the unknown disturbance. Subsequently, the auxiliary control signal was introduced to compensate the impact of actuator faults on train. Then, when designing adaptive laws, the variations of basic resistance parameters were fully considered. By integrating adaptive laws with the adaptive fault-tolerant controller, the effect of disturbances on multi-train systems can be dynamically eliminated. The Lyapunov method was established to prove the stability of multi-train systems. Simulation results show that the proposed adaptive fault-tolerant control strategy can maintain the stability of multi-train systems and achieve cooperation of trains with actuator faults.