PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
March 10, 2026IET Control Theory and Applications0 citationsOpen Access

An Adaptation Mechanism of Model Reference Adaptive System Based on Variable Structure Control for Online Parameter Estimation of IPMSM

View Full Paper
EAErtuğrul AteşBTBurak TekgünMBMURAT BARUT

Key Points

  • The study aims to enhance online parameter estimation techniques for IPMSMs using an adaptive system approach.
  • Developed MRAS estimators based on stator currents
  • Implemented variable structured control for adaptation
  • Conducted simulation studies for performance assessment
  • Compared with traditional MRAS using a fixed-gain PI controller
  • VSC-based MRAS algorithms outperformed traditional PI-based algorithms
  • Improved accuracy and reliability in estimating parameters
  • Eliminated reliance on fixed-gain PI controllers

Abstract

ABSTRACT This study introduces stator currents‐based model reference adaptive system (MRAS) estimators that employ variable structured control (VSC) in the adaptation mechanism to enable the online estimation of stator resistance and permanent magnet (PM) flux in interior permanent magnet synchronous motors (IPMSMs). These MRAS estimators estimate stator resistance and PM flux by analysing the error between the stator currents measured as the reference model and the stator currents generated by the adaptive model. The performance of the proposed estimators is assessed through simulation studies. Furthermore, the proposed approach is compared to a conventional MRAS employing a fixed‐gain proportional‐integral (PI) controller. Simulation results and error analyses indicate that the VSC‐based MRAS algorithms outperform traditional PI‐based MRAS in terms of accuracy and reliability. Additionally, the proposed method eliminates the reliance on a fixed‐gain PI controller, a common component in conventional MRAS systems.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Ateş et al. (2026) studied this question.

synapsesocial.com/papers/69af958570916d39fea4d2a9https://doi.org/10.1049/cth2.70080
Ask AI
Helpful
Bookmark
Share
View Full Paper