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Control-Oriented Learning for Dynamic Tracking and Stability Analysis of Soft Pneumatic Actuators

arXiv · AI, language, vision and robotics · article · Aug 25, 2026 · UTC

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. Experimen

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