Application of a recurrent wavelet fuzzy-neural network in the positioning control of a magnetic-bearing mechanism. (August 2016)

• Record Type: Journal Article
• Authors: Chen, Syuan-Yi; Hung, Ying-Chih; Hung, Yi-Hsuan; Wu, Chien-Hsun
• Is Part Of: Computers & electrical engineering. Volume 54(2016)
• Page Start: 147
• Page End: 158
• Publication Date: 2016-08
• Subjects: Fuzzy Neural Network (FNN) -- Magnetic Bearing (MB) -- Particle Swarm Optimization (PSO) -- Positioning control; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2015.11.022
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: A new recurrent wavelet fuzzy neural network (RWFNN) controller is proposed. RWFNN is adopted to control the rotor position of a thrust magnetic bearing (TMB). The online learning algorithm of RWFNN is derived using back-propagation method. The adaptive learning rates are performed via improved particle swarm optimization. Numerical simulations show the validity of TMB using the RWFNN controller. 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. Graphical abstract: