用Transformer实现电机前馈控制的少样本上下文学习。
Learning Nonlinear Systems In-Context: From Synthetic Data to Real-World Motor Control
- 分离信号表示与系统行为，支持少样本微调和单次上下文学习。
- 仅需少量真实数据即可准确预测复杂负载下的电机动态。
- 适合需要高效适应物理系统的控制场景，如机器人与自动化。
大型语言模型在上下文学习（ICL）方面表现出色，但尚未应用于信号处理系统。受其设计启发，本文首次提出基于Transformer的模型架构，用于电机前馈控制这一关键任务——传统PI控制与基于物理的方法在非线性及复杂负载条件下表现不佳。该模型在大量合成线性与非线性系统上预训练，能够将信号表示与系统行为解耦，从而实现少样本微调与单次上下文学习。实验表明，该方法可跨多种电机负载配置泛化，将未调参样本转化为精准前馈预测，显著优于经典PI控制器与基于物理的前馈基线。结果证明，上下文学习能有效连接合成预训练与真实世界适应性，为物理系统的数据高效控制开辟新路径。
原文摘要 · Abstract (English)
LLMs have shown strong in-context learning (ICL) abilities, but have not yet been extended to signal processing systems. Inspired by their design, we have proposed for the first time ICL using transformer models applicable to motor feedforward control, a critical task where classical PI and physics-based methods struggle with nonlinearities and complex load conditions. We propose a transformer based model architecture that separates signal representation from system behavior, enabling both few-shot finetuning and one-shot ICL. Pretrained on a large corpus of synthetic linear and nonlinear systems, the model learns to generalize to unseen system dynamics of real-world motors only with a handful of examples. In experiments, our approach generalizes across multiple motor load configurations, transforms untuned examples into accurate feedforward predictions, and outperforms PI controllers and physics-based feedforward baselines. These results demonstrate that ICL can bridge synthetic pretraining and real-world adaptability, opening new directions for data efficient control of physical systems.
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