RBFNN-Based Adaptive Iterative Learning Fault-Tolerant Control for Subway Trains With Actuator Faults and Speed Constraint

In this article, a radial basis function neural network-based adaptive iterative learning fault-tolerant control (RBFNN-AILFTC) algorithm is developed for subway trains subject to the time-iteration-dependent actuator faults and speed constraint by using the multiple-point-mass dynamic model. First,...

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Veröffentlicht in:	IEEE transactions on systems, man, and cybernetics. Systems Jg. 51; H. 9; S. 5785 - 5799
Hauptverfasser:	Liu, Genfeng, Hou, Zhongsheng
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.09.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Actuators Adaptation models Adaptive control Adaptive iterative learning control (AILC) Adaptive systems Algorithms Constraint modelling Convergence Dynamic models Fault tolerance fault-tolerant control Heuristic algorithms Iterative methods Machine learning multiple-point-mass dynamic model Neural networks Public transportation Radial basis function radial basis function neural network (RBFNN) Schedules speed constraint subway train Time dependence Timetables Tracking errors Trains Vehicle dynamics
ISSN:	2168-2216, 2168-2232
Online-Zugang:	Volltext
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