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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Vydáno v:International journal of robust and nonlinear control Ročník 33; číslo 3; s. 1772 - 1784
Hlavní autoři: Wang, Qian, Jin, Shangtai, Hou, Zhongsheng, Gao, Guangzhuo
Médium: Journal Article
Jazyk:angličtina
Vydáno: Bognor Regis Wiley Subscription Services, Inc 01.02.2023
Témata:
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
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Shrnutí: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‐related full‐form dynamic linearization data model (FFDLDM), which relies on the input, output, and faults data of the subway train system. The actuator faults and unknown nonlinear terms of the subway train system are estimated by the projection algorithm. Then, in the time domain, the model‐free adaptive control (MFAC) algorithm is utilized and unknown controller parameters are estimated by the RBFNN algorithm. In the iteration domain, a feedforward D‐type iterative learning control (ILC) algorithm is added to the outer loop of the MFAC algorithm. Finally, the theoretical analysis proves that the speed and position tracking errors of the subway train are bounded, the simulations demonstrate the effectiveness of the proposed composite control scheme of the subway train.
Bibliografie:Funding information
National Natural Science Foundation of China, Grant/Award Numbers: 62073025; 62273028; Beijing Natural Science Foundation, Grant/Award Number: L201015; Fundamental Research Funds for the Central Universities, Grant/Award Number: 2020YJS010
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ISSN:1049-8923
1099-1239
DOI:10.1002/rnc.6447