Cooperative Adaptive Iterative Learning Fault-Tolerant Control Scheme for Multiple Subway Trains

In this article, a cooperative adaptive iterative learning fault-tolerant control (CAILFTC) algorithm with the radial basis function neural network (RBFNN) is proposed for multiple subway trains subject to the time-iteration-dependent actuator faults by using the multiple-point-mass dynamics model....

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Veröffentlicht in:IEEE transactions on cybernetics Jg. 52; H. 2; S. 1098 - 1111
Hauptverfasser: Liu, Genfeng, Hou, Zhongsheng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.02.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Actuators
Adaptive control
Adaptive iterative learning control (ILC)
Algorithms
Convergence
cooperative control
Fault tolerance
Fault tolerant control
Headways
Iterative learning control
Iterative methods
Machine learning
multiple subway trains
multiple-point-mass dynamics model
Neural networks
Public transportation
Radial basis function
Radial basis function networks
radial basis function neural network (RBFNN)
Tracking errors
ISSN:2168-2267, 2168-2275, 2168-2275
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Zusammenfassung:In this article, a cooperative adaptive iterative learning fault-tolerant control (CAILFTC) algorithm with the radial basis function neural network (RBFNN) is proposed for multiple subway trains subject to the time-iteration-dependent actuator faults by using the multiple-point-mass dynamics model. First, an RBFNN is utilized to cope with the unknown nonlinearity of the subway train system. Next, a composite energy function (CEF) technique is applied to obtain the convergence property of the presented CAILFTC, which can guarantee that all train speed tracking errors are asymptotic convergence along the iteration axis; meanwhile, the headway distances of neighboring subway trains are kept in a safety range. Finally, the effectiveness of theoretical studies is verified through a subway train simulation.
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2020.2986006