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

• DOI: 10.48550/arxiv.2502.00532
• Publication Date: 2025-01-01
• Authors: Elele, Martin Joe...; Pau, Danilo; Zhuang, Shixin; Facchinetti, Tullio
• Keywords: FOS: Computer and information sciences; Computer Science - Machine Learning; FOS: Electrical engineering, electronic engineering, information engineering; Systems and Control (eess.SY); Electrical Engineering and Systems Science - Systems and Control; Machine Learning (cs.LG)

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.<br/>This paper has been submitted to the EDGE AI Research Symposium 2025 (https://conf.researchr.org/home/tinyml-symp-2025#Call-for-papers previously known as tinyML Research Symposium). It was peer reviewed and camera ready updated accordingly to reviewer's feedback. It will be presented in EDGE AI FOUNDATION Austin 2025 (https://www.edgeaifoundation.org/events/austin-2024)<br/>