Enhancing Field-Oriented Control of Electric Drives with Tiny Neural Network Optimized for Micro-controllers

TL;DR

This paper presents a tiny neural network integrated into motor control systems, significantly improving precision and reducing overshoot in real-time applications on micro-controllers.

Contribution

It introduces a lightweight neural network optimized for micro-controllers to enhance field-oriented control of electric drives, surpassing traditional PI controllers.

Findings

Reduced overshoot by up to 87.5%

Pruned model achieved complete overshoot elimination

Effective neural network deployment on resource-constrained micro-controllers

Abstract

The deployment of neural networks on resource-constrained micro-controllers has gained momentum, driving many advancements in Tiny Neural Networks. This paper introduces a tiny feed-forward neural network, TinyFC, integrated into the Field-Oriented Control (FOC) of Permanent Magnet Synchronous Motors (PMSMs). Proportional-Integral (PI) controllers are widely used in FOC for their simplicity, although their limitations in handling nonlinear dynamics hinder precision. To address this issue, a lightweight 1,400 parameters TinyFC was devised to enhance the FOC performance while fitting into the computational and memory constraints of a micro-controller. Advanced optimization techniques, including pruning, hyperparameter tuning, and quantization to 8-bit integers, were applied to reduce the model's footprint while preserving the network effectiveness. Simulation results show the proposed approach significantly reduced overshoot by up to 87.5%, with the pruned model achieving complete overshoot elimination, highlighting the potential of tiny neural networks in real-time motor control applications.