Direct Data-Driven Predictive Control for a Three-dimensional Cable-Driven Soft Robotic Arm
This addresses the challenge of dynamic control for soft robots, which is crucial for applications requiring safety and adaptability, though it is incremental as it extends an existing method to a new domain.
The paper tackled precise control of a 3D cable-driven soft robotic arm by applying Data-enabled Predictive Control (DeePC), achieving superior accuracy, robustness, and adaptability compared to a model-based baseline in tasks like fixed-point regulation and trajectory tracking.
Soft robots offer significant advantages in safety and adaptability, yet achieving precise and dynamic control remains a major challenge due to their inherently complex and nonlinear dynamics. Recently, Data-enabled Predictive Control (DeePC) has emerged as a promising model-free approach that bypasses explicit system identification by directly leveraging input-output data. While DeePC has shown success in other domains, its application to soft robots remains underexplored, particularly for three-dimensional (3D) soft robotic systems. This paper addresses this gap by developing and experimentally validating an effective DeePC framework on a 3D, cable-driven soft arm. Specifically, we design and fabricate a soft robotic arm with a thick tubing backbone for stability, a dense silicone body with large cavities for strength and flexibility, and rigid endcaps for secure termination. Using this platform, we implement DeePC with singular value decomposition (SVD)-based dimension reduction for two key control tasks: fixed-point regulation and trajectory tracking in 3D space. Comparative experiments with a baseline model-based controller demonstrate DeePC's superior accuracy, robustness, and adaptability, highlighting its potential as a practical solution for dynamic control of soft robots.