Model-Free Predictive Sliding Mode Control for PMSM Drives Using Ultra-Local Method

Yao Wei, Dongliang Ke, Fengxiang Wang, Marcelo Lobo Heldwein

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

4 Scopus citations

Abstract

Since some physical parameters of the motor are time-varying, the realized control performances are inevitably affected by the parameter mismatches in the sliding mode control (SM C). To fully eliminate the influences caused by the parameter mismatches and enhance the robustness of the conventional SMC, an ultra-local-based model-free predictive SMC is proposed in this paper, and used as the current controller in the permanent magnet synchronous motor (PMSM) driving system. A priori model is replaced by the ultra-local, in which physical parameters of the plant are not required anymore. The unknown terms of the plant are summarized as a single variable and online estimated by an extended state observer (ESO) to maintain a well model accuracy. The effectiveness and correctness are demonstrated by the simulation and experimental results, as well as the advantage of current quality and speed integral of time and absolute error (ITAE) with suitable robustness.

Original languageEnglish
Title of host publicationIECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society
PublisherIEEE Computer Society
ISBN (Electronic)9798350331820
DOIs
StatePublished - 2023
Event49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023 - Singapore, Singapore
Duration: 16 Oct 202319 Oct 2023

Publication series

NameIECON Proceedings (Industrial Electronics Conference)
ISSN (Print)2162-4704
ISSN (Electronic)2577-1647

Conference

Conference49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023
Country/TerritorySingapore
CitySingapore
Period16/10/2319/10/23

Keywords

  • Model-free control
  • data-driven model
  • motor driving
  • sliding mode control
  • ultra-local

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