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：软体机器人在安全性与适应性方面具有显著优势，但由于其固有的复杂非线性动力学特性，实现精确的动态控制仍是一个重大挑战。近年来，数据驱动预测控制作为一种有前景的无模型方法崭露头角，它通过直接利用输入输出数据，绕过了显式的系统辨识过程。尽管数据驱动预测控制在其他领域已取得成功，但其在软体机器人中的应用仍待深入探索，尤其对于三维软体机器人系统而言。本文针对这一空白，在一个三维缆索驱动软体臂上开发并实验验证了一个有效的数据驱动预测控制框架。具体而言，我们设计并制造了一种软体机械臂，其采用厚壁管状骨架以保证稳定性，具有带大空腔的致密硅胶本体以兼顾强度与柔顺性，并配备刚性端盖以实现可靠端部固定。基于此平台，我们实现了结合奇异值分解降维的数据驱动预测控制方法，并应用于两个关键控制任务：三维空间中的定点调节与轨迹跟踪。与基于模型的基线控制器进行的对比实验表明，数据驱动预测控制在精度、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解决方案的潜力。