Model‐free adaptive and iterative learning composite control for subway train under actuator faults

In this article, the model‐free adaptive and iterative learning composite control (CMFAC‐ILC) is proposed to ensure the speed and position tracking for the subway train system subjected to the iteration‐time‐varying actuator faults. First, a nonlinear subway train system is transformed into a faults...

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Bibliographic Details
Published in:	International journal of robust and nonlinear control Vol. 33; no. 3; pp. 1772 - 1784
Main Authors:	Wang, Qian, Jin, Shangtai, Hou, Zhongsheng, Gao, Guangzhuo
Format:	Journal Article
Language:	English
Published:	Bognor Regis Wiley Subscription Services, Inc 01.02.2023
Subjects:	Actuators Adaptive algorithms Adaptive control Algorithms D‐type iterative learning control Faults faults‐related full‐form dynamic linearization data model Feedforward control Iterative methods Machine learning model‐free adaptive control Parameter estimation RBFNN algorithm Strip searches subway train control Tracking errors Trains
ISSN:	1049-8923, 1099-1239
Online Access:	Get full text
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