Sliding mode control of SPMSM drivers: An online gain tuning approach with unknown system parameters

Jin Woo Jung, Viet Quoc Leu, Dong Quang Dang, Han Ho Choi, Tae Heoung Kim

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

This paper proposes an online gain tuning algorithm for a robust sliding mode speed controller of surface-mounted permanent magnet synchronous motor (SPMSM) drives. The proposed controller is constructed by a fuzzy neural network control (FNNC) term and a sliding mode control (SMC) term. Based on a fuzzy neural network, the first term is designed to approximate the nonlinear factors while the second term is used to stabilize the system dynamics by employing an online tuning rule. Therefore, unlike conventional speed controllers, the proposed control scheme does not require any knowledge of the system parameters. As a result, it is very robust to system parameter variations. The stability evaluation of the proposed control system is fully described based on the Lyapunov theory and related lemmas. For comparison purposes, a conventional sliding mode control (SMC) scheme is also tested under the same conditions as the proposed control method. It can be seen from the experimental results that the proposed SMC scheme exhibits better control performance (i.e., faster and more robust dynamic behavior, and a smaller steady-state error) than the conventional SMC method.

Original languageEnglish
Article numberJPE 14-5-19
Pages (from-to)980-988
Number of pages9
JournalJournal of Power Electronics
Volume14
Issue number5
DOIs
StatePublished - 2014

Keywords

  • Fuzzy neural network control (FNNC)
  • Sliding mode control (SMC)
  • Speed control
  • Surface-mounted permanent magnet synchronous motor (SPMSM)
  • System parameter variations

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