用1400参数小模型提升电机控制精度，适合嵌入式部署。
Enhancing Field-Oriented Control of Electric Drives with Tiny Neural Network Optimized for Micro-controllers
- 设计1400参数小神经网络TinyFC，替代传统控制器
- 仿真显示超调减少87.5%，剪枝后完全消除超调
- 专为微控制器优化，支持量化与压缩，适合实时控制
在资源受限的微控制器上部署神经网络已成为趋势，推动了微型神经网络的发展。本文提出一种轻量级前馈神经网络TinyFC，集成于永磁同步电机（PMSM）的矢量控制（FOC）系统中。传统的比例-积分（PI）控制器虽简单，但难以处理非线性动态，影响控制精度。为此，设计了一个仅含1,400个参数的微型网络，在满足微控制器计算与内存限制的同时，显著提升控制性能。采用剪枝、超参数调优及8位整数量化等先进优化技术，有效降低模型体积并保持性能。仿真结果表明，该方法可将超调量最大减少87.5%，剪枝后的模型实现超调完全消除，充分展示了微型神经网络在实时电机控制中的应用潜力。
原文摘要 · Abstract (English)
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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