Optimization of Urban Rail Automatic Train Operation System Based on RBF Neural Network Adaptive Terminal Sliding Mode Fault Tolerant Control

Optimization of Urban Rail Automatic Train Operation System Based on RBF Neural Network Adaptive Terminal Sliding Mode Fault Tolerant Control

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 co...

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Bibliographic Details
Main Authors: Junxia Yang, Youpeng Zhang, Yuxiang Jin
Format: Article
Language:English
Published: MDPI AG 2021-08-01
Series:Applied System Innovation
Subjects:
automatic train operation system (ATO)
radial basis function neural network (RBFNN)
adaptive terminal sliding mode control (ATSMC)
fault-tolerant control (FTC)
tracking error
Online Access:https://www.mdpi.com/2571-5577/4/3/51

Internet

https://www.mdpi.com/2571-5577/4/3/51

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