用数据驱动方法建模软体肌肉，实现高精度实时控制。
Nonlinear Spectral Modeling and Control of Soft-Robotic Muscles from Data
- 基于谱流形理论，从响应数据中学习输入输出映射。
- 相比纯反馈或前馈控制，跟踪误差显著降低。
- 无需物理建模，适合快速部署于软体关节控制。
人工肌肉是柔性肌骨机器人的关键，但其非线性多物理场动态使控制复杂化。液压增强静电（HASEL）致动器作为一类软人工肌肉，性能优异却存在记忆效应和迟滞。本文提出一种基于谱流形（SSM）理论的数据驱动降维与控制策略。在绝热条件下（输入变化远慢于内部瞬态），系统轨迹迅速收敛至低维慢流形。我们仅从受迫响应轨迹中学习该流形上的显式输入-输出映射，避免了可能诱发迟滞的衰减实验。将该SSM模型应用于对偶式HASEL离合器关节的实时控制，在相同设置下，跟踪误差显著低于纯反馈与纯前馈基线。这一记录-控制工作流程实现了软肌肉及肌肉驱动关节的快速表征与高性能控制，无需详尽的物理建模。
原文摘要 · Abstract (English)
Artificial muscles are essential for compliant musculoskeletal robotics but complicate control due to nonlinear multiphysics dynamics. Hydraulically amplified electrostatic (HASEL) 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 (SSM) 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 from forced-response trajectories alone, avoiding decay experiments that can trigger hysteresis. We deploy the SSM-based model for real-time control of an antagonistic HASEL-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.
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