用数据直接控制3D软体机械臂，精度和鲁棒性优于传统方法。
Direct Data-Driven Predictive Control for a Three-dimensional Cable-Driven Soft Robotic Arm
- 基于输入输出数据直接控制，跳过复杂建模。
- 在固定点调节和三维轨迹跟踪任务中误差更小。
- 适合需要快速适配的柔性机器人应用。
软体机器人在安全性和适应性方面具有显著优势，但其固有的复杂非线性动力学使得精确动态控制仍面临重大挑战。近年来，数据驱动预测控制（DeePC）作为一种无需显式系统辨识的模型无关方法崭露头角，通过直接利用输入输出数据实现控制。尽管DeePC在其他领域已取得成功，但在三维（3D）软体机器人中的应用仍较少被探索。本文针对这一空白，构建并实验验证了一种有效的DeePC框架，应用于一个3D缆绳驱动软体机械臂。具体而言，设计并制造了带有厚管状主干以增强稳定性、密集硅胶主体含大腔体以兼顾强度与柔韧性的机械臂，并采用刚性端盖实现牢固连接。在此平台上，利用奇异值分解（SVD）进行降维，实现了两种关键控制任务：固定点调节和三维空间轨迹跟踪。与基准模型基控制器相比，实验表明DeePC在准确性、鲁棒性和适应性方面表现更优，展现出作为软体机器人动态控制实用解决方案的巨大潜力。
原文摘要 · Abstract (English)
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.
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