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

Syuan Yi Chen, Tung Sheng Liu

Research output: Contribution to journalArticle

22 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1043-1054
Number of pages12
JournalIET Electric Power Applications
Volume11
Issue number6
DOIs
Publication statusPublished - 2017 Jul 1

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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