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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Vydáno v:	IEEE transactions on systems, man, and cybernetics. Systems Ročník 51; číslo 9; s. 5785 - 5799
Hlavní autoři:	Liu, Genfeng, Hou, Zhongsheng
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.09.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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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Abstract	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.
AbstractList	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.
Author	Liu, Genfeng Hou, Zhongsheng
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SubjectTerms	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
Title	RBFNN-Based Adaptive Iterative Learning Fault-Tolerant Control for Subway Trains With Actuator Faults and Speed Constraint
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