Abstract
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.
Abstract (translated)
软体机器人在医疗保健领域，特别是辅助护理中的应用越来越广泛，这得益于其固有的安全性和适应性。然而，由于非线性动力学和时间延迟的存在，控制软体机器人的难度较大，尤其是在像为患者提供护理的软体机械臂这样的应用场景中。本文提出了一种基于学习的方法来近似非线性状态预测器（史密斯预测器），旨在提高两模块软机器人手臂在短固有输入延迟情况下的跟踪性能。该方法采用核递归最小二乘追踪器(KRLST)进行系统的动力学在线学习，并利用勒让德延迟网络(LDN)压缩过去的输入历史，以实现有效的延迟补偿。实验结果表明，在与基于模型的非线性控制器相比较时，本文提出的方法在跟踪性能方面有了显著提升。统计分析确认了这些改进的重要性。该方法计算效率高且能够在线适应，适用于实际场景，并突显了其在辅助护理应用中使软体机器人控制更加安全和准确方面的潜力。
URL
https://arxiv.org/abs/2504.12428
