面向控制的柔性气动驱动器动态跟踪与稳定性分析学习方法
Control-Oriented Learning for Dynamic Tracking and Stability Analysis of Soft Pneumatic Actuators
- Vanderbilt University（范德堡大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出面向控制的数据驱动框架，将柔性气动驱动器行为分解为非线性静态平衡模型与EDMDc辨识的线性残差动力学模型，实现高精度轨迹跟踪、实时避障及稳定性分析验证。
AI中文摘要：
柔性气动驱动器具备固有柔顺性与安全交互性，但因高度非线性、分布式动力学特性，建模与控制难度较大。本文提出一种面向控制的数据驱动建模与控制框架，将驱动器行为分解为非线性静态平衡模型，以及采用带控制的扩展动态模态分解（EDMDc）辨识得到的线性残差动力学模型。该表示可通过增广线性模型实现前馈补偿、任务空间反馈控制与局部闭环稳定性分析。实验中，低速（约10 mm/s）轨迹跟踪的均方根误差（RMSE）约为1 mm，高速（约100 mm/s）时RMSE低于10 mm；该框架还实现了对峰值加速度超25 m/s²的高动态用户生成参考的稳定跟踪，同时完成实时避障。最后，所提稳定性分析通过准确预测稳定、边缘及不稳定工作状态得到实验验证。这些结果表明，结构化的面向控制学习为柔性驱动器控制提供了准确且实用的框架。
英文摘要：
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.
