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ANN-Based Online Parameter Correction for PMSM Control Using Sphere Decoding Algorithm.

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Summary
This summary is machine-generated.

This study introduces an artificial neural network (ANN) to compensate for parameter variations in Permanent Magnet Synchronous Motor (PMSM) drives. The ANN enhances controller performance and robustness against uncertainties in flux linkage and inductance.

Keywords:
artificial neural networkmodel predictive controlpermanent magnet synchronous motorsphere decoding algorithm

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Area of Science:

  • Electrical Engineering
  • Control Systems
  • Artificial Intelligence

Background:

  • Permanent Magnet Synchronous Motor (PMSM) drives are susceptible to performance degradation due to parameter variations like flux linkage and inductance under operational uncertainties.
  • Accurate parameter estimation is crucial for maintaining optimal performance and efficiency in PMSM drives.

Purpose of the Study:

  • To develop and evaluate an artificial neural network (ANN) based method for online estimation and compensation of parameter mismatches in PMSM drives.
  • To enhance the robustness and performance of PMSM drives against variations in flux linkage and inductance.

Main Methods:

  • Data generation using Sphere Decoding Algorithm-based Model Predictive Control (SDA-MPC) with parameter mismatches ranging from ±50%.
  • Offline training of an ANN to establish a mapping between measured features and parameter estimates.
  • Online deployment of the trained ANN to dynamically update controller parameters within the SDA-MPC framework.

Main Results:

  • ANN-based compensation improved current tracking and reduced Total Harmonic Distortion (THD) under various parameter mismatch conditions.
  • In specific scenarios, particularly with overestimated inductance, THD could increase compared to nominal operation.
  • The ANN demonstrated adaptive capabilities, returning the controller to baseline performance as parameters normalized.

Conclusions:

  • Data-driven adaptation using ANNs offers enhanced robustness for PMSM drives with minimal computational overhead.
  • The proposed method shows potential for improving PMSM drive performance under realistic operating uncertainties.
  • Further research, including hardware-in-the-loop testing and temperature effect analysis, is warranted.