PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
April 17, 2026Measurement and Control0 citationsOpen Access

A novel variable exponential reaching law-based nonlinear integral sliding mode control for surface-mounted permanent magnet synchronous motors

View Full Paper
TWTeng WangQLQ LiuKMKeqilao Meng

Key Points

  • The aim is to improve the performance of surface-mounted permanent magnet synchronous motors by enhancing robustness and response time.
  • Developed a nonlinear sliding surface to manage large error windup
  • Introduced a variable exponential reaching law for faster convergence
  • Conducted experiments to compare performance against conventional methods
  • Demonstrated improved dynamic response
  • Achieved better disturbance rejection
  • Enhanced steady-state accuracy compared to traditional control techniques

Abstract

Surface-mounted Permanent Magnet Synchronous Motors (SPMSMs) are key components in industrial servo drives, due to their high precision and efficiency. However, their performance is often degraded by parameter variations and load disturbances, leading to poor robustness and slow response. To address integral saturation and chattering, this paper proposes a Nonlinear Integral Sliding Mode Control with a Variable Exponential Reaching Law (NISMC-VERL). The method features: (1) a nonlinear sliding surface that prevents windup under large errors and improves precision under small errors; (2) a variable exponential reaching law that accelerates convergence when far from the sliding surface and suppresses chattering near it. Experimental results show that the proposed method outperforms conventional techniques in dynamic response, disturbance rejection, and steady-state accuracy, demonstrating its strong potential for industrial applications.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Wang et al. (2026) studied this question.

synapsesocial.com/papers/69e1cf985cdc762e9d85889chttps://doi.org/10.1177/00202940261440334
Ask AI
Helpful
Bookmark
Share
View Full Paper