Modeless Magnetic Bearing System Tracking Using an Adaptive Fuzzy Hermite Neural Network Method

This paper proposes a reference model adaptive fuzzy Hermite neural-network controller (RAFHNC) for position tracking of a modeless magnetic bearing system (MBS). Magnetic bearings evolved from traditional bearings and can eliminate the shortcomings of traditional bearings by using electromagnetic f...

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Bibliographic Details
Published in:IEEE sensors journal 2019-07, Vol.19 (14), p.5904-5915
Main Authors: Mao, Wei-Lung, Chu, Chao-Ting
Format: Article
Language:English
Subjects:
Adaptation models
Adaptive control
Adaptive fuzzy Hermite neural network estimator
Adaptive systems
Artificial neural networks
Bearing (direction)
Bearings
Control methods
Control stability
Controllers
Electromagnetic forces
Friction reduction
Fuzzy control
Fuzzy logic
Fuzzy systems
Knowledge management
Load modeling
Lyapunov stability
magnetic bearing system (MBS)
Magnetic bearings
Magnetic levitation
Mathematical model
Mathematical models
Neural networks
Parameters
reference model
reflective optical sensor
Root-mean-square errors
Training
Uncertainty
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Summary:This paper proposes a reference model adaptive fuzzy Hermite neural-network controller (RAFHNC) for position tracking of a modeless magnetic bearing system (MBS). Magnetic bearings evolved from traditional bearings and can eliminate the shortcomings of traditional bearings by using electromagnetic forces to reduce friction during use. The MBS is nonlinear, for which system parameters are difficult to obtain accurately. This paper proposes the RAFHNC for precision position tracking in an MBS. The RAFHNC has three important characteristics. First, the RAFHNC does not require the MBS parameters to track a reference model accurately. Second, the RAFHNC uses Hermite functions to replace the Gaussian function, this reduces training time in the membership level. Third, the RAFHNC uses an online self-tuning fuzzy Hermite neural network estimator to resolve lumped uncertainty in the MBS. The fuzzy Hermite neural network incorporates expert knowledge, reducing training time on controller parameters. Finally, the Lyapunov stability and system adaptive laws are used to guarantee convergence. The output responses of the RAFHNC and adaptive reference model sliding-mode controller (ARSC) methods were compared. The RAFHNC method exhibited improved output responses compared with the ARSC method in terms of root mean squared error (RMSE) index. It can reduce problems associated with external loads and system lumped uncertainty in MBS.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2019.2906730