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
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Zusammenfassung: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, the RBFNN is utilized to approximate the time-iteration-dependent unknown nonlinearity of the subway train system; then, the iterative learning mechanism is used to tackle the outstanding repetitive operational pattern of a subway train which runs from one station to the next strictly according to the operation timetable schedule every day within the finite time interval, and the adaptive mechanism is designed for dealing with the time and the iteration-varying factors of the subway train. Second, a barrier composite energy function technique is exploited to obtain the convergence property of the proposed RBFNN-AILFTC scheme for subway train system, which can guarantee that the tracking error is asymptotic convergence along the iteration axis, meanwhile keep the speed profile of the subway train system satisfies the constraint. Finally, a subway train simulation is shown to verify the effectiveness of the theoretical studies.
Bibliographie:ObjectType-Article-1
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content type line 14
ISSN:2168-2216
2168-2232
DOI:10.1109/TSMC.2019.2957299