Application of a recurrent wavelet fuzzy-neural network in the positioning control of a magnetic-bearing mechanism

Syuan Yi Chen; Ying Chih Hung; Yi Hsuan Hung; Chien Hsun Wu

doi:10.1016/j.compeleceng.2015.11.022

Application of a recurrent wavelet fuzzy-neural network in the positioning control of a magnetic-bearing mechanism

Syuan Yi Chen^*, Ying Chih Hung, Yi Hsuan Hung, Chien Hsun Wu

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

23 Citations (Scopus)

Abstract

A new recurrent wavelet fuzzy neural network (RWFNN) with adaptive learning rates is proposed to control the rotor position on the axial direction of a thrust magnetic bearing (TMB) mechanism in this study. First, the dynamic analysis of the TMB with differential driving mode (DDM) is derived. Because the dynamic characteristics and system parameters of the TMB mechanism are high nonlinear and time-varying, the RWFNN, which integrates wavelet transforms with fuzzy rules, is proposed to achieve precise positioning control of the TMB. For the designed RWFNN controller, the online learning algorithm is derived using back-propagation method. Moreover, since the improper selection of learning rates for the RWFNN will deteriorate the control performance, an improved particle swarm optimization (IPSO) is adopted to adapt the learning rates of the RWFNN on-line. Numerical simulations show the validity of TMB system using the proposed RWFNN controller with IPSO under the occurrence of uncertainties.

Original language	English
Pages (from-to)	147-158
Number of pages	12
Journal	Computers and Electrical Engineering
Volume	54
DOIs	https://doi.org/10.1016/j.compeleceng.2015.11.022
Publication status	Published - 2016 Aug 1

Keywords

Fuzzy Neural Network (FNN)
Magnetic Bearing (MB)
Particle Swarm Optimization (PSO)
Positioning control

ASJC Scopus subject areas

Control and Systems Engineering
General Computer Science
Electrical and Electronic Engineering

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10.1016/j.compeleceng.2015.11.022

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title = "Application of a recurrent wavelet fuzzy-neural network in the positioning control of a magnetic-bearing mechanism",

abstract = "A new recurrent wavelet fuzzy neural network (RWFNN) with adaptive learning rates is proposed to control the rotor position on the axial direction of a thrust magnetic bearing (TMB) mechanism in this study. First, the dynamic analysis of the TMB with differential driving mode (DDM) is derived. Because the dynamic characteristics and system parameters of the TMB mechanism are high nonlinear and time-varying, the RWFNN, which integrates wavelet transforms with fuzzy rules, is proposed to achieve precise positioning control of the TMB. For the designed RWFNN controller, the online learning algorithm is derived using back-propagation method. Moreover, since the improper selection of learning rates for the RWFNN will deteriorate the control performance, an improved particle swarm optimization (IPSO) is adopted to adapt the learning rates of the RWFNN on-line. Numerical simulations show the validity of TMB system using the proposed RWFNN controller with IPSO under the occurrence of uncertainties.",

keywords = "Fuzzy Neural Network (FNN), Magnetic Bearing (MB), Particle Swarm Optimization (PSO), Positioning control",

author = "Chen, {Syuan Yi} and Hung, {Ying Chih} and Hung, {Yi Hsuan} and Wu, {Chien Hsun}",

note = "Publisher Copyright: {\textcopyright} 2015 Elsevier Ltd",

year = "2016",

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doi = "10.1016/j.compeleceng.2015.11.022",

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T1 - Application of a recurrent wavelet fuzzy-neural network in the positioning control of a magnetic-bearing mechanism

AU - Chen, Syuan Yi

AU - Hung, Ying Chih

AU - Hung, Yi Hsuan

AU - Wu, Chien Hsun

PY - 2016/8/1

Y1 - 2016/8/1

N2 - A new recurrent wavelet fuzzy neural network (RWFNN) with adaptive learning rates is proposed to control the rotor position on the axial direction of a thrust magnetic bearing (TMB) mechanism in this study. First, the dynamic analysis of the TMB with differential driving mode (DDM) is derived. Because the dynamic characteristics and system parameters of the TMB mechanism are high nonlinear and time-varying, the RWFNN, which integrates wavelet transforms with fuzzy rules, is proposed to achieve precise positioning control of the TMB. For the designed RWFNN controller, the online learning algorithm is derived using back-propagation method. Moreover, since the improper selection of learning rates for the RWFNN will deteriorate the control performance, an improved particle swarm optimization (IPSO) is adopted to adapt the learning rates of the RWFNN on-line. Numerical simulations show the validity of TMB system using the proposed RWFNN controller with IPSO under the occurrence of uncertainties.

AB - A new recurrent wavelet fuzzy neural network (RWFNN) with adaptive learning rates is proposed to control the rotor position on the axial direction of a thrust magnetic bearing (TMB) mechanism in this study. First, the dynamic analysis of the TMB with differential driving mode (DDM) is derived. Because the dynamic characteristics and system parameters of the TMB mechanism are high nonlinear and time-varying, the RWFNN, which integrates wavelet transforms with fuzzy rules, is proposed to achieve precise positioning control of the TMB. For the designed RWFNN controller, the online learning algorithm is derived using back-propagation method. Moreover, since the improper selection of learning rates for the RWFNN will deteriorate the control performance, an improved particle swarm optimization (IPSO) is adopted to adapt the learning rates of the RWFNN on-line. Numerical simulations show the validity of TMB system using the proposed RWFNN controller with IPSO under the occurrence of uncertainties.

KW - Fuzzy Neural Network (FNN)

KW - Magnetic Bearing (MB)

KW - Particle Swarm Optimization (PSO)

KW - Positioning control

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VL - 54

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JO - Computers and Electrical Engineering

JF - Computers and Electrical Engineering

ER -

Application of a recurrent wavelet fuzzy-neural network in the positioning control of a magnetic-bearing mechanism

Abstract

Keywords

ASJC Scopus subject areas

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