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

## TL;DR

This paper introduces an artificial neural network to correct parameter mismatches in electric motor control, improving performance under uncertain conditions.

## Contribution

A novel online parameter correction method for PMSM using an ANN trained with Sphere Decoding Algorithm-based Model Predictive Control.

## Key findings

- ANN-based compensation improves current tracking and THD in most mismatch conditions.
- Overestimation of inductance can increase THD relative to nominal operation.
- The ANN adapts to return to baseline performance when parameters normalize.

## Abstract

This work addresses parameter mismatch in Permanent Magnet Synchronous Motor (PMSM) drives, focusing on performance degradation caused by variations in flux linkage and inductance arising under realistic operating uncertainties. An artificial neural network (ANN) is trained to estimate these parameter shifts and update the controller model online. The procedure comprises three steps: (i) data generation using Sphere Decoding Algorithm-based Model Predictive Control (SDA-MPC) across a mismatch range of ±50%; (ii) offline ANN training to map measured features to parameter estimates; and (iii) online ANN deployment to update model parameters within the SDA-MPC loop. MATLAB /Simulink simulations show that ANN-based compensation can improve current tracking and THD under many mismatch conditions, although in some cases—particularly when inductance is overestimated—THD may increase relative to nominal operation. When parameters return to nominal values the ANN adapts accordingly, steering the controller back toward baseline performance. The data-driven adaptation enhances robustness with modest computational overhead. Future work includes hardware-in-the-loop (HIL) testing and explicit experimental study of temperature-dependent effects.