为软气动执行器设计可控制的建模与控制框架，提升轨迹跟踪精度与稳定性。
Control-Oriented Learning for Dynamic Tracking and Stability Analysis of Soft Pneumatic Actuators
- 将非线性动态分解为静态平衡与线性残差模型，用EDMDc方法识别。
- 低速时跟踪误差约1 mm RMSE，高速时低于10 mm RMSE，峰值加速度超25 m/s²仍稳定。
- 支持实时避障和实验验证的稳定性分析，适合机器人控制与柔性系统研究者。
软气动执行器具有固有柔性和安全交互优势，但因其高度非线性、分布式的动态特性，建模与控制仍具挑战。本文提出一种面向控制的数据驱动建模与控制框架，将执行器行为分解为非线性静态平衡模型与通过扩展动态模态分解带控制（EDMDc）识别的线性残差动态模型。该表示形式支持前馈补偿、任务空间反馈控制及基于增广线性模型的局部闭环稳定性分析。实验表明，在低速（约10 mm/s）下轨迹跟踪的均方根误差（RMSE）约为1 mm，高速（约100 mm/s）时低于10 mm RMSE。该框架还能稳定跟踪峰值加速度超过25 m/s²的高动态用户生成参考轨迹，并同时实现实时障碍物避让。最后，所提稳定性分析经实验验证，能准确预测稳定、临界与不稳定工作区。结果表明，结构化的控制导向学习为软执行器控制提供了准确且实用的解决方案。
原文摘要 · Abstract (English)
Soft pneumatic actuators offer inherent compliance and safe interaction but remain difficult to model and control because of their highly nonlinear, distributed dynamics. We present a control-oriented data-driven modeling and control framework that decomposes actuator behavior into a nonlinear static equilibrium model and a linear residual dynamics model identified using Extended Dynamic Mode Decomposition with control (EDMDc). This representation enables feedforward compensation, task-space feedback control, and local closed-loop stability analysis through an augmented linear model. Experiments achieve approximately 1 mm root mean square error (RMSE) during low-speed (approximately 10 mm/s) trajectory tracking and below 10 mm RMSE at higher speeds (approximately 100 mm/s). The framework further achieves stable tracking of highly dynamic user-generated references with peak accelerations exceeding 25 m/s^2 while simultaneously performing real-time obstacle avoidance. Finally, the proposed stability analysis is experimentally validated by accurately predicting stable, marginal, and unstable operating regimes. These results demonstrate that structured, control-oriented learning provides an accurate and practical framework for soft actuator control.
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