用物理嵌入的神经微分方程建模气动肌肉耦合非线性特性。
Physics-Embedded Neural ODEs for Learning Antagonistic Pneumatic Artificial Muscle Dynamics
- 将物理结构嵌入神经微分方程，学习对抗性气动肌肉动力学。
- 在196组未见条件下预测准确，平均R²达0.88。
- 适合需要精准阻抗控制的软体机器人研究者。
气动人工肌肉（PAMs）为软体可穿戴、辅助及交互机器人提供柔顺驱动。当以对抗方式布置时，可通过共同收缩实现可变阻抗，但表现出耦合、非线性及滞后动力学，给建模与控制带来挑战。本文提出一种混合神经常微分方程（Neural ODE）框架，将物理结构嵌入对抗性PAM动力学的可学习模型中。该方法结合参数化关节力学与气动状态动力学，辅以神经网络力项，捕捉对抗耦合与速率依赖的滞回特性。前向模型在29个选定共同收缩条件下训练，对196组保留条件下的关节运动与腔室压力进行预测，平均R²达0.88。逆向公式由所学动力学推导，离线计算期望运动与刚度轮廓的压力指令，并在执行中闭环跟踪。实验验证表明，在126-176 N/mm范围内实现可靠刚度控制，且在不同工作速度下保持一致阻抗行为，优于静态模型在高速下的性能退化。
原文摘要 · Abstract (English)
Pneumatic artificial muscles (PAMs) enable compliant actuation for soft wearable, assistive, and interactive robots. When arranged antagonistically, PAMs can provide variable impedance through co-contraction but exhibit coupled, nonlinear, and hysteretic dynamics that challenge modeling and control. This paper presents a hybrid neural ordinary differential equation (Neural ODE) framework that embeds physical structure into a learned model of antagonistic PAM dynamics. The formulation combines parametric joint mechanics and pneumatic state dynamics with a neural network force component that captures antagonistic coupling and rate-dependent hysteresis. \rev{The forward model was trained on 29 selected co-contraction conditions and predicted joint motion and chamber pressures over 196 held-out conditions with a mean R$^2$ of 0.88.} An inverse formulation, derived from the learned dynamics, computes pressure commands offline for desired motion and stiffness profiles, tracked in closed loop during execution. Experimental validation demonstrates reliable stiffness control across 126-176 N/mm and consistent impedance behavior across operating velocities, in contrast to a static model, which shows degraded stiffness consistency at higher velocities.
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