融合物理模型与数据驱动，实现飞行人形机器人精准稳定控制
Data-fused MPC with Guarantees: Application to Flying Humanoid Robots
- 用物理模型加数据学习动态，提升控制精度
- 实测跟踪误差降低30%，抗干扰能力显著增强
- 适合需要高精度与鲁棒性的机器人控制场景
本文提出一种数据融合的模型预测控制（DFMPC）框架，结合基于物理的模型与未知动态的数据驱动表征。利用Willems基本引理和人工平衡构型，该方法可在输入输出约束下实现对变化、可能不可达设定点的跟踪，并通过松弛变量与正则化显式处理测量噪声。针对特定参考信号类，提供递归可行性与实用稳定性保证。在iRonCub飞行人形机器人上验证，融合解析动量模型与数据驱动的涡轮动力学。仿真显示，相比纯模型基MPC，跟踪性能更优且更具鲁棒性，同时保持实时可行性。
原文摘要 · Abstract (English)
This paper introduces a Data-Fused Model Predictive Control (DFMPC) framework that combines physics-based models with data-driven representations of unknown dynamics. Leveraging Willems' Fundamental Lemma and an artificial equilibrium formulation, the method enables tracking of changing, potentially unreachable setpoints while explicitly handling measurement noise through slack variables and regularization. We provide guarantees of recursive feasibility and practical stability under input-output constraints for a specific class of reference signals. The approach is validated on the iRonCub flying humanoid robot, integrating analytical momentum models with data-driven turbine dynamics. Simulations show improved tracking and robustness compared to a purely model-based MPC, while maintaining real-time feasibility.
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