Neural Networks-Based Sliding Mode Fault-Tolerant Control for High-Speed Trains With Bounded Parameters and Actuator Faults

Abstract

In this paper, novel adaptive fault-tolerant control (FTC) algorithms are put forward to address the position and velocity tracking control problems of high-speed trains (HSTs). The basic running resistances, additional resistances and interactive forces between the connected carriages are taken into account. On account of the system uncertainties, a novel multidimensional sliding mode surface with time-varying parameters estimated by the adaptive technique is proposed. And neural networks (NNS) are made use of approximating the additional resistances viewed as a bounded disturbance. Two cases for system parameters are taken into consideration: 1) Parameters with unknown boundary are unknown; 2) Parameters with known upper and lower boundary are unknown. As for the former, the unknown parameters are formulated via formal mathematical expression with indicator functions and the adaptive technique is introduced; As for the latter, a continuous functions related to the above multidimensional sliding mode surface is defined and the adaptive technique with Project functions is designed, which guarantees saturation characteristics of the presented controller. The closed-loop system stability can be proved by means of the Lyapunov theory and the feasibility and effectiveness of the presented control schemes can be revealed via simulation experiments.