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A novel dynamic prescribed performance fuzzy-neural backstepping control for PMSM under step load.

Xuechun Hu1, Yu Xia2, Zsófia Lendek3

  • 1School of Mechanical Engineering, Guizhou University, Guiyang 550025, China.

Neural Networks : the Official Journal of the International Neural Network Society
|May 30, 2025
PubMed
Summary

This study introduces a new control method for permanent magnet synchronous motor (PMSM) systems facing changing parameters and load disturbances. The approach enhances system stability and performance, ensuring precise operation.

Keywords:
Fuzzy neural backstepping controlPMSMPrescribed performance functionSpeed function

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Area of Science:

  • Electrical Engineering
  • Control Systems
  • Robotics

Background:

  • Permanent magnet synchronous motor (PMSM) systems often face challenges with time-varying parameters and input constraints, especially under step load disturbances.
  • Traditional prescribed performance functions can lead to issues like exceeding predefined errors, control singularity, and system instability during load changes.

Purpose of the Study:

  • To propose a dynamic prescribed performance fuzzy-neural backstepping control approach for PMSM systems.
  • To address performance degradation caused by nonlinear time-varying parameters and input constraints.
  • To improve the transient and steady-state performance of PMSM systems under step load.

Main Methods:

  • A novel finite-time asymmetric dynamic prescribed performance function (FADPPF) was developed to overcome limitations of traditional methods.
  • A backstepping controller was designed integrating a speed function (SF) and a fuzzy neural network (FNN).
  • The FNN approximates uncertain nonlinear system functions, while the SF and FADPPF ensure system performance.

Main Results:

  • The proposed FADPPF demonstrated dynamic self-adjusting capabilities and effectiveness under step load conditions.
  • Lyapunov analysis confirmed the stability of the developed control strategy.
  • Simulation results validated the feasibility and superiority of the proposed control scheme compared to existing methods.

Conclusions:

  • The dynamic prescribed performance fuzzy-neural backstepping control approach effectively manages PMSM systems with time-varying parameters and input constraints.
  • The novel FADPPF is crucial for maintaining system stability and performance during load variations.
  • The integrated control strategy offers a robust and superior solution for PMSM control applications.