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

• URL: http://arxiv.org/abs/2502.00532v1
• Date: Sat, 01 Feb 2025 19:16:51 GMT
• Title: Enhancing Field-Oriented Control of Electric Drives with Tiny Neural Network Optimized for Micro-controllers
• Authors: Martin Joel Mouk Elele, Danilo Pau, Shixin Zhuang, Tullio Facchinetti,
• Abstract summary: This paper introduces a tiny feed-forward neural network, TinyFC, integrated into the Field-Oriented Control (FOC) of Permanent Magnet Synchronous Motors (PMSMs) 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.
• Score: 0.8328638943795448
• License:
• 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.

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