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

2020 ◽  
Vol 69 (2) ◽  
pp. 1353-1362 ◽  
Author(s):  
Hairong Dong ◽  
Xue Lin ◽  
Shigen Gao ◽  
Baigen Cai ◽  
Bin Ning
Keyword(s):  
Neural Networks ◽  
High Speed ◽  
Fault Tolerant ◽  
Sliding Mode ◽  
Fault Tolerant Control ◽  
Actuator Faults ◽  
High Speed Trains

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

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xiangyu Kong ◽  
Tong Zhang
Keyword(s):  
Neural Network ◽  
Time Delay ◽  
High Speed ◽  
Fault Tolerant ◽  
Sliding Mode ◽  
Tracking Error ◽  
Fault Tolerant Control ◽  
Actuator Faults ◽  
High Speed Trains ◽  
Critic Neural Network

This article investigates the cooperative fault-tolerant control problem for multiple high-speed trains (MHSTs) with actuator faults and communication delays. Based on the actor-critic neural network, a distributed sliding mode fault-tolerant controller is designed for MHSTs to solve the problem of actuator faults. To eliminate the negative effects of unknown disturbances and time delay on train control system, a distributed radial basis function neural network (RBFNN) with adaptive compensation term of the error is designed to approximate the nonlinear disturbances and predict the time delay, respectively. By calculating the tracking error online, an actor-critic structure with RBFNN is used to estimate the switching gain of the distributed controller, which reduces the chattering phenomenon caused by sliding mode control. The global stability and ultimate bounded of all signals of the closed-loop system are proposed with strict mathematic proof. Simulations show that the proposed method has superior effectiveness and robustness compared with other fault-tolerant control methods, which ensures the safe operation of MHSTs under moving block conditions.

Fault-Tolerant Control of High-Speed Trains with Parameter Uncertainty

2013 ◽  
Vol 325-326 ◽  
pp. 1099-1105 ◽  
Author(s):  
Tao Tao ◽  
Hong Ze Xu
Keyword(s):  
Parameter Uncertainty ◽  
High Speed ◽  
Fault Tolerant ◽  
Fault Tolerant Control ◽  
Fault System ◽  
Linear Matrix ◽  
Actuator Faults ◽  
Response Curves ◽  
Tracking Controller ◽  
High Speed Trains

This paper studies the robust fault-tolerant control problem against actuator faults and parameter uncertainty for High-Speed Trains. First, models of actuator faults and parameter uncertainty are presented. Then a robust fault-tolerant tracking controller design method is developed. This method is based on the mixed Linear Matrix Inequalities (LMI)/Lyapunov stability theory. Tracking control examples and simulations are given, and the response curves of the fault system and the system with the fault-tolerant tracking controller are presented.

scholarly journals Model-Independent Adaptive Fault-Tolerant Tracking Control for High-Speed Trains with Actuator Saturation

2019 ◽  
Vol 9 (19) ◽  
pp. 4146 ◽  
Author(s):  
Chuanfang Xu ◽  
Xiyou Chen ◽  
Lin Wang
Keyword(s):  
Tracking Control ◽  
High Speed ◽  
Actuator Saturation ◽  
Fault Tolerant ◽  
Sliding Mode ◽  
Upper Bounds ◽  
Model Parameters ◽  
Actuator Faults ◽  
High Speed Trains ◽  
Model Independent

This paper investigates the fault-tolerant tracking control problem of high-speed trains (HSTs) subject to unknown model parameters with unavailable uncertainties, unmeasurable additional disturbance, and unpredictable actuator faults constrained by actuator saturation. An adaptive passive fault-tolerant tracking control strategy based on variable-gain proportion-integral-derivative (PID)-type sliding mode surface is proposed to handle the problem. Unknown model parameters, gains of the PID-type sliding mode surface, and upper bounds of the lumped system uncertainty which includes additional disturbance, modeling uncertainties, and uncertainties resulting from actuator faults, are estimated online by adaptive technology. The input saturation (actuator output saturation) constraint is handled by introducing an auxiliary signal. The proposed controller can compensate for the effects of the lumped uncertainty and the actuator faults effectively. Moreover, the controller is model-independent, which means it requires no prior knowledge of model parameters and upper bounds of the lumped uncertainty, and does not depend upon fault detection and diagnosis module. The asymptotic stability of the closed-loop train system is demonstrated by Lyapunov theory. Good fault-tolerant tracking capacity, effective anti-actuator saturation ability, and strong robustness of the proposed controller are verified via numerical simulation.

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