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.

翻译：人工肌肉对于顺应性肌肉骨骼机器人至关重要，但由于其非线性多物理场动力学特性，使得控制变得复杂。液压放大静电（HASEL）驱动器作为一类软体人工肌肉，具有高性能，但表现出记忆效应和迟滞现象。本文提出一种基于谱子流形（SSM）理论的数据驱动降阶与控制策略。在绝热状态下，即输入变化相对于固有瞬态过程足够缓慢时，系统轨迹会迅速收敛到一个低维慢流形。我们仅通过强迫响应轨迹，在该流形上学习一个显式的输入-输出映射，从而避免了可能触发迟滞的衰减实验。我们部署了基于SSM的模型，用于一个拮抗式HASEL离合器关节的实时控制。在相同设置下，与纯反馈和纯前馈基线方法相比，该方法显著降低了跟踪误差。这种记录与控制工作流程无需详细的基于物理的建模，即可实现软体肌肉及肌肉驱动关节的快速表征与高性能控制。