用学习方法补偿软机器人延迟，提升护理机器人的控制精度。
Learning-based Delay Compensation for Enhanced Control of Assistive Soft Robots
- 用KRLST在线学动态，用LDN压缩历史输入以高效补偿延迟。
- 实验显示跟踪误差显著降低，统计检验结果显著改善。
- 适合需要实时自适应控制的医疗辅助软机器人场景。
软机器人因其固有的安全性和适应性，在医疗领域，尤其是辅助护理中日益受到重视。由于非线性动力学和时间延迟的存在，控制软机器人极具挑战性，特别是在患者护理的软机械臂应用中。本文提出一种基于学习的方法，近似非线性状态预测器（Smith Predictor），以提升双模块软机械臂在短固有输入延迟下的跟踪性能。该方法采用核递归最小二乘追踪器（KRLST）实现系统动态的在线学习，并利用勒让德延迟网络（LDN）压缩过去的输入历史，实现高效的延迟补偿。实验结果表明，相比基线模型化非线性控制器，跟踪性能得到显著提升，统计分析证实改进具有显著性。该方法计算效率高且支持在线自适应，适用于实际应用场景，突显其在辅助护理中实现更安全、更精确控制软机器人的潜力。
原文摘要 · Abstract (English)
Soft robots are increasingly used in healthcare, especially for assistive care, due to their inherent safety and adaptability. Controlling soft robots is challenging due to their nonlinear dynamics and the presence of time delays, especially in applications like a soft robotic arm for patient care. This paper presents a learning-based approach to approximate the nonlinear state predictor (Smith Predictor), aiming to improve tracking performance in a two-module soft robot arm with a short inherent input delay. The method uses Kernel Recursive Least Squares Tracker (KRLST) for online learning of the system dynamics and a Legendre Delay Network (LDN) to compress past input history for efficient delay compensation. Experimental results demonstrate significant improvement in tracking performance compared to a baseline model-based non-linear controller. Statistical analysis confirms the significance of the improvements. The method is computationally efficient and adaptable online, making it suitable for real-world scenarios and highlighting its potential for enabling safer and more accurate control of soft robots in assistive care applications.
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