Adaptive Fault-Tolerant Sliding Mode Control for High-Speed Trains With Actuator Faults Under Strong Winds

Abstract

This paper addresses the fault-tolerant control problem for high-speed trains (HSTs) with actuator faults under strong winds. For the healthy system, a non-singular fast terminal sliding mode surface is introduced into the controller, which ensures the system fast converge to the equilibrium point with finite time, and the radial basis function neural network (RBFNN) with the adaptive compensation term of the error is used to approximate the unknown nonlinear disturbances of the train system in strong winds and predict the time delay generated in the train communication network. Through the RBFNN observer established specifically for actuator failures, the real effective factors are identified. Combining with the identified effective factors and predicted time delay, an adaptive fault-tolerant sliding mode control method is established for train systems. With dynamic parameters and position update strategy guided by negative gradient thought, an improved particle swarm optimization algorithm is proposed to optimize the uncertain parameters of control method, which weakens the chattering phenomenon from the sliding mode controller and improves the control accuracy. Simulation results show that the proposed method has better tracking performance and real-time performance compared with other fault-tolerant control methods under various operating conditions, which ensures the running safety of HSTs under different strong winds.

Distributed Cooperative Sliding Mode Fault-Tolerant Control for Multiple High-Speed Trains Based on Actor-Critic Neural Network

“…e proposed method can solve the unknown actuator faults and the system uncertainties at the same time by designing the adaptive compensation law. Compared with reference [21], this method does not need the neural network fault observer and cannot be affected by fault diagnosis error, which can well deal with the uncertainty caused by fault and simplifies the design complexity of the fault-tolerant controller in [21]. It should be noted that on the basis of existing cooperative fault-tolerant control methods for multiple trains [9], the proposed method further considers communication delays during signal transmission, and the distributed neural network prediction model is introduced into the control scheme to compensate for the influence of network delay on the train control system.…”

“…where i � 1, 2, ..., N is the number of trains running on the same railway, x i (t) and v i (t) are the position and speed of train i, respectively, χ is the acceleration factor, u i (t) is the unit control force of train i, and f i (v i (t)) and d i (t) are the unit basic resistance and additional resistance of train i, respectively, which can be described as follows [21]:…”

Improved Generalized Predictive Control for High-Speed Train Network Systems Based on EMD-AQPSO-LS-SVM Time Delay Prediction Model

“…where y(k) is the speed of train, χ is the acceleration coefficient, u(k − τ ca ) is the unit control force of train, τ ca � (τ ca /T), τ ca is the forward channel time delay, T is the sampling period, d(k) is the unit additional resistance caused by the complicated operating environment such as wind, tunnel, and curve, and f 0 (y(k)) is the unit general resistance of train, which can be described as [3]…”

“…where α 0 (k), α 1 (k), and α 2 (k) with high uncertainty change constantly with the change of operating condition, which makes the train traction control system have obvious multiple working conditions and nonlinear characteristics [3]. In practical application, the high-speed train network system is composed of an automatic train operation (ATO) system, traction control system, and sensors.…”

“…In addition to the end-to-end time delay in the TCN, there is also the time delay generated by signal processing and control logic judgment, etc. If the time delay is too long, it will greatly affect the stability of the control system [3]. e network environment of HSTs is complex, and key systems such as traction and braking have obvious nonlinear characteristics [4].…”