2021
DOI: 10.3390/asi4030051
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Optimization of Urban Rail Automatic Train Operation System Based on RBF Neural Network Adaptive Terminal Sliding Mode Fault Tolerant Control

Abstract: Aiming at the problem of the large tracking error of the desired curve for the automatic train operation (ATO) control strategy, an ATO control algorithm based on RBF neural network adaptive terminal sliding mode fault-tolerant control (ATSM-FTC-RBFNN) is proposed to realize the accurate tracking control of train operation curve. On the one hand, considering the state delay of trains in operation, a nonlinear dynamic model is established based on the mechanism of motion mechanics. Then, the terminal sliding mo… Show more

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Cited by 6 publications
(4 citation statements)
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“…MAO et al 13 proposed a new adaptive fault-tolerant sliding mode control scheme considering disturbance and actuator failure. Although sliding mode control [14][15][16][17][18] has a good tracking effect, it can easily produce a chattering phenomenon. Although some correction methods, such as the saturation function method, can effectively reduce the chattering phenomenon, they will sacrifice the advantage of outstanding anti-disturbance performance.…”
Section: Introductionmentioning
confidence: 99%
“…MAO et al 13 proposed a new adaptive fault-tolerant sliding mode control scheme considering disturbance and actuator failure. Although sliding mode control [14][15][16][17][18] has a good tracking effect, it can easily produce a chattering phenomenon. Although some correction methods, such as the saturation function method, can effectively reduce the chattering phenomenon, they will sacrifice the advantage of outstanding anti-disturbance performance.…”
Section: Introductionmentioning
confidence: 99%
“…There are few works describing the applications of artificial intelligence methods in Fault-Tolerant control systems. The works in the literature describe only the use of shallow neural networks, mainly perceptron networks, for IM [23,24]. This approach does not require a priori knowledge of the facility and provides very good results, which is its great advantage.…”
Section: Introductionmentioning
confidence: 99%
“…This approach does not require a priori knowledge of the facility and provides very good results, which is its great advantage. Work [23] presents a passive FTC system based on RBF neural networks. The passive controller adaptively compensates for external disturbances.…”
Section: Introductionmentioning
confidence: 99%
“…In the past few years, many train operation control methods have been studied, most of which have focused on single high-speed trains. These have included but are not limited to fault-tolerant control [2], neural network control [3], sliding mode control [4], fuzzy control [5], and robust control [6]. In [7], a self-organizing radial basis function neural network (RBFNN) was adopted to approximate the nonlinear factors of the train, and the non-singular terminal sliding mode control strategy was designed to control the train.…”
Section: Introductionmentioning
confidence: 99%