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

Content Provider	IET Digital Library
Author	Chen, Syuan Yi Liu, Tung Sheng
Abstract	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.
Starting Page	1043
Ending Page	1054
Page Count	12
ISSN	17518660
Volume Number	11
e-ISSN	17518679
Issue Number	Issue 6, Jul (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-epa/11/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-epa.2016.0819
Journal	IET Electric Power Applications
Publisher Date	2017-07-01
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Asymmetric Membership Function Controller Control of Electric Power System Control Process Field-oriented Control Fuzzy Control Intelligent Control Intelligent Tracking Control Learning Algorithm Linear Machine Linear Motors Machine Control Machine Vector Control Neurocontrol Parameter Learning Permanent Magnet Linear Synchronous Motor Permanent Magnet Motors PMLSM Servo Drive System Position Control Self-evolving Probabilistic Fuzzy Neural Network Servo Drive System Spatial Variables Control SPFNN SPFNN-AMF Control System Structure Learning Synchronous Machine Synchronous Motors
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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