Intelligent tracking control of a PMLSM using self-evolving probabilistic fuzzy neural network

This study presents a self-evolving probabilistic fuzzy (PF) neural network with asymmetric membership function (SPFNN-AMF) controller for the position servo control of a permanent magnet linear synchronous motor (PMLSM) servo drive system. In the beginning, the dynamic model for the PMLSM is analysed on the basis of field-oriented control. Subsequently, an SPFNN-AMF control system, which integrates the advantages of self-evolving NN, PF logic system, and AMF, is proposed to handle vagueness, randomness, and time-varying uncertainties of the PMLSM servo drive system during the control process. For the SPFNN-AMF, the proposed learning algorithm consists of the structure learning and parameter learning in which the former is used to grow and prune the fuzzy rules automatically, whereas the latter is utilised to train the network parameters dynamically. Finally, detailed experimental results of two position commands tracking at different operation conditions demonstrate the validity and robustness of the proposed SPFNN-AMF for controlling the PMLSM servo drive system.

[1]  Chun-Fei Hsu,et al.  Intelligent control of chaotic systems via self-organizing Hermite-polynomial-based neural network , 2014, Neurocomputing.

[2]  Mei-Yung Chen,et al.  High-Precision Motion Control for a Linear Permanent Magnet Iron Core Synchronous Motor Drive in Position Platform , 2014, IEEE Transactions on Industrial Informatics.

[3]  Brigitte Chebel-Morello,et al.  Linear feature selection and classification using PNN and SFAM neural networks for a nearly online diagnosis of bearing naturally progressing degradations , 2015, Eng. Appl. Artif. Intell..

[4]  Zhi Liu,et al.  A Probabilistic Neural-Fuzzy Learning System for Stochastic Modeling , 2008, IEEE Transactions on Fuzzy Systems.

[5]  Yong-Nong Chang,et al.  Adaptive backstepping control for permanent magnet linear synchronous motor servo drive , 2015 .

[6]  Yang-Yin Lin,et al.  A recurrent self-evolving fuzzy neural network with local feedbacks and its application to dynamic system processing , 2010, Fuzzy Sets Syst..

[7]  Jung Hyun Choi,et al.  Robust tracking performance of linear induction motor-based automatic picking system using a high-gain disturbance observer , 2016 .

[8]  Sehoon Oh,et al.  A High-Precision Motion Control Based on a Periodic Adaptive Disturbance Observer in a PMLSM , 2015, IEEE/ASME Transactions on Mechatronics.

[9]  Yi-Sheng Huang,et al.  Based on Direct Thrust Control for Linear Synchronous Motor Systems , 2009, IEEE Transactions on Industrial Electronics.

[10]  Faa-Jeng Lin,et al.  An intelligent control for linear ultrasonic motor using interval type-2 fuzzy neural network , 2008 .

[11]  Hualin Tan,et al.  Adaptive backstepping control and friction compensation for AC servo with inertia and load uncertainties , 2003, IEEE Trans. Ind. Electron..

[12]  Kenji Suzuki,et al.  Driving Method of Permanent-Magnet Linear Synchronous Motor With the Stationary Discontinuous Armature for Long-Distance Transportation System , 2012, IEEE Transactions on Industrial Electronics.

[13]  Chunyan Miao,et al.  A probabilistic fuzzy approach to modeling nonlinear systems , 2011, Neurocomputing.

[14]  Muhammad Faz Rahman,et al.  Optimal, Combined Speed, and Direct Thrust Control of Linear Permanent Magnet Synchronous Motors , 2016, IEEE Transactions on Energy Conversion.

[15]  Xiao-Jun Zeng,et al.  An improved approach of self-organising fuzzy neural network based on similarity measures , 2012, Evol. Syst..

[16]  Faa-Jeng Lin,et al.  Self-constructing recurrent fuzzy neural network for DSP-based permanent-magnet linear-synchronous-motor servodrive , 2006 .

[17]  Peter Mutschler,et al.  Short primary linear drive designed for synchronous and induction operation in different sections , 2014 .

[18]  Fayez F. M. El-Sousy,et al.  Self-Organizing Recurrent Fuzzy Wavelet Neural Network-Based Mixed $\rm{H}_{2}\rm{/ H}_{\rm{\infty }}$ Adaptive Tracking Control for Uncertain Two-Axis Motion Control System , 2016, IEEE Transactions on Industry Applications.

[19]  Syuan-Yi Chen,et al.  Recurrent Functional-Link-Based Fuzzy Neural Network Controller With Improved Particle Swarm Optimization for a Linear Synchronous Motor Drive , 2009, IEEE Transactions on Magnetics.

[20]  Ming Cheng,et al.  Design and Analysis of a New Fault-Tolerant Linear Permanent-Magnet Motor for Maglev Transportation Applications , 2012, IEEE Transactions on Applied Superconductivity.

[21]  Ching-Hung Lee,et al.  Performance enhancement for neural fuzzy systems using asymmetric membership functions , 2009, Fuzzy Sets Syst..

[22]  Jonq-Chin Hwang,et al.  Digital signal processor-based probabilistic fuzzy neural network control of in-wheel motor drive for light electric vehicle , 2012 .

[23]  Manfredi Maggiore,et al.  Control of a 5DOF Magnetically Levitated Positioning Stage , 2008, IEEE Transactions on Control Systems Technology.

[24]  Yi-Hsuan Hung,et al.  Speed Control of Vane-Type Air Motor Servo System Using Proportional-Integral-Derivative-Based Fuzzy Neural Network , 2016, Int. J. Fuzzy Syst..

[25]  Xiang Li,et al.  Machine health condition prediction via online dynamic fuzzy neural networks , 2014, Eng. Appl. Artif. Intell..

[26]  Manoj Tripathy,et al.  Probabilistic neural-network-based protection of power transformer , 2007 .