外观
论文arXiv 2601.032471 个月前
基于数据的软机器人肌肉非线性谱建模与控制
原文链接：Nonlinear spectral modeling and control of soft-robotic muscles from data（arXiv 2601.03247 · 发布日期：2026-09-16）
Nonlinear spectral modeling and control of soft-robotic muscles from data
针对电液软人工肌肉非线性、记忆与滞后难控问题，基于谱子流形从受迫响应学习降阶模型，并在对抗式电液离合关节实时控制中验证。
核心要点
- 人工肌肉支撑柔顺肌骨机器人，但非线性多物理动态、记忆效应和滞后使控制复杂。
- 在绝热条件下利用轨迹收敛到低维慢流形，从受迫响应直接学习输入到输出映射。
- 该方法避免由大阶跃输入产生的衰减实验，减少不代表正常工况的滞后影响。
- 模型用于对抗式电液离合关节实时控制，相比纯反馈和纯前馈基线显著降低跟踪误差。
验证范围与局限：验证范围为输入相对内在瞬态缓慢的绝热条件，以及对抗式电液离合关节的实时控制。
英文摘要 (Abstract)
Artificial muscles are essential for compliant musculoskeletal robotics but complicate control due to nonlinear multiphysics dynamics. Electrohydraulic actuators, a class of soft artificial muscles, offer high performance but exhibit memory effects and hysteresis. Here we present a data-driven reduction and control strategy grounded in spectral submanifold theory. In the adiabatic regime, where inputs vary slowly relative to intrinsic transients, trajectories rapidly converge to a low-dimensional slow manifold. We learn an explicit input-to-output map on this manifold directly from forced-response trajectories, eliminating the need for decay experiments generated by large step inputs, which can induce pronounced hysteretic behavior and are often unrepresentative of normal operating conditions. We deploy the spectral-submanifold-based model for real-time control of an antagonistic electrohydraulic-clutch joint. This approach yields a substantial reduction in tracking error compared to feedback-only and feedforward-only baselines under identical settings. This record-and-control workflow enables rapid characterization and high-performance control of soft muscles and muscle-driven joints without detailed physics-based modeling.
作者：Leonardo Bettini, Amirhossein Kazemipour, Robert Kevin Katzschmann, George Haller
来源
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