TY - JOUR
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
N1 - Funding Information:
The authors would like to acknowledge the financial support of the Ministry of Science and Technology in Taiwan, R.O.C. through its grant MOST 103-2218-E-003-001.
Publisher Copyright:
© 2015 Elsevier Ltd
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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U2 - 10.1016/j.compeleceng.2015.11.022
DO - 10.1016/j.compeleceng.2015.11.022
M3 - Article
AN - SCOPUS:84952040286
SN - 0045-7906
VL - 54
SP - 147
EP - 158
JO - Computers and Electrical Engineering
JF - Computers and Electrical Engineering
ER -