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

Syuan Yi Chen, Tung Sheng Liu

研究成果: 雜誌貢獻文章同行評審

29 引文 斯高帕斯(Scopus)

摘要

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.

原文英語
頁(從 - 到)1043-1054
頁數12
期刊IET Electric Power Applications
11
發行號6
DOIs
出版狀態已發佈 - 2017 七月 1

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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