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.

翻译：软体机器人因其在安全性和适应性方面的显著优势而备受关注，但因其固有的复杂非线性动力学特性，实现精确动态控制仍是一项重大挑战。近年来，数据驱动预测控制（DeePC）作为一种新兴的无模型方法，通过直接利用输入-输出数据绕过了显式系统辨识过程。尽管DeePC已在其他领域展现出成功应用，但其在软体机器人，特别是三维（3D）软体机器人系统中的应用仍有待深入探索。本文通过开发并实验验证一个适用于三维线驱动软体臂的DeePC框架来填补这一空白。具体而言，我们设计并制造了一种软体机械臂：采用厚管骨架确保稳定性，利用带大空腔的致密硅胶体提供强度与柔韧性，并配备刚性端盖实现可靠固定。基于该平台，我们实现了结合奇异值分解（SVD）降维的DeePC方法，用于两项关键控制任务：三维空间中的定点调节与轨迹跟踪。与基于模型的基准控制器进行的对比实验表明，DeePC在精度、鲁棒性和适应性方面均表现更优，凸显了其作为软体机器人动态控制实用方案的潜力。