Intelligent Online Auto-Tuning Technique for IPMSM Servo Drive

Faa Jeng Lin, Shih Gang Chen, Hsiao Tse Chou, Jyun Ru Lin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

A real-time moment of inertia identification technique using wavelet fuzzy neural network (WFNN) for an interior permanent magnet synchronous motor (IPMSM) servo drive is proposed in this study. The estimated moment of inertia will be used in the online design of an integral-proportional (IP) speed controller to achieve the gains auto-tuning of the IPMSM servo drive. In this study, first, the dynamic analysis of a field-oriented control (FOC) IPMSM servo drive system with an IP speed controller is studied. Then, a heuristic approach using the WFNN is proposed for the real-time identification of the moment of inertia of the IPMSM servo drive system. Moreover, the network structure and the convergence analysis of the WFNN are introduced. Furthermore, an IPMSM servo drive based on a high performance digital signal processor (DSP) is developed. Finally, from the experimental results, the gains of the IP speed controller can be effectively tuned online at different operating conditions.

Original languageEnglish
Title of host publication2019 IEEE 4th International Future Energy Electronics Conference, IFEEC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728131535
DOIs
StatePublished - Nov 2019
Event4th IEEE International Future Energy Electronics Conference, IFEEC 2019 - Singapore, Singapore
Duration: 25 Nov 201928 Nov 2019

Publication series

Name2019 IEEE 4th International Future Energy Electronics Conference, IFEEC 2019

Conference

Conference4th IEEE International Future Energy Electronics Conference, IFEEC 2019
Country/TerritorySingapore
CitySingapore
Period25/11/1928/11/19

Keywords

  • Interior permanent magnet synchronous motor
  • online gain auto-tuning
  • wavelet fuzzy neural network

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