Online Autotuning Technique for IPMSM Servo Drive by Intelligent Identification of Moment of Inertia

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

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

In this article, a real-time moment of inertia identification technique using Petri probabilistic fuzzy neural network with an asymmetric membership function (PPFNN-AMF) for an interior permanent magnet synchronous motor (IPMSM) servo drive is proposed. The estimated moment of inertia will be used in the online design of an integral-proportional (IP) speed controller to achieve the gains autotuning of the IPMSM servo drive. In the proposed method, the dynamic analysis of a field-oriented control IPMSM servo drive system with an IP speed controller is constructed first. Then, a heuristic approach using the PPFNN-AMF 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 PPFNN-AMF are devised and derivated. Furthermore, an IPMSM servo drive based on a high-performance digital signal processor is developed. Finally, from the experimental results, the gains of the IP speed controller can be tuned online effectively at different operating conditions with robust control characteristics.

Original languageEnglish
Article number8954652
Pages (from-to)7579-7590
Number of pages12
JournalIEEE Transactions on Industrial Informatics
Volume16
Issue number12
DOIs
StatePublished - Dec 2020

Keywords

  • Asymmetric membership function (AMF)
  • Petri probabilistic fuzzy neural network (PPFNN)
  • interior permanent magnet synchronous motor (IPMSM)
  • online gain autotuning

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