Chi Haozhen, Li Xuefang, Liang Wenyu, Cao Jiawei, Ren Qinyuan
College of Control Science and Engineering, Zhejiang University, Hangzhou, China.
Department of Electrical and Electronic Engineering, Imperial College London, London, United Kingdom.
Front Robot AI. 2019 Nov 12;6:113. doi: 10.3389/frobt.2019.00113. eCollection 2019.
Soft robots have recently received much attention with their infinite degrees of freedoms and continuously deformable structures, which allow them to adapt well to the unstructured environment. A new type of soft actuator, namely, dielectric elastomer actuator (DEA) which has several excellent properties such as large deformation and high energy density is investigated in this study. Furthermore, a DEA-based soft robot is designed and developed. Due to the difficulty of accurate modeling caused by nonlinear electromechanical coupling and viscoelasticity, the iterative learning control (ILC) method is employed for the motion trajectory tracking with an uncertain model of the DEA. A type ILC algorithm is proposed for the task. Furthermore, a knowledge-based model framework with kinematic analysis is explored to prove the convergence of the proposed ILC. Finally, both simulations and experiments are conducted to demonstrate the effectiveness of the ILC, which results show that excellent tracking performance can be achieved by the soft crawling robot.
软机器人因其具有无限的自由度和可连续变形的结构，近年来受到了广泛关注，这使得它们能够很好地适应非结构化环境。本研究对一种新型软致动器，即具有大变形和高能量密度等优异特性的介电弹性体致动器（DEA）进行了研究。此外，还设计并开发了一种基于DEA的软机器人。由于非线性机电耦合和粘弹性导致精确建模困难，因此采用迭代学习控制（ILC）方法对DEA不确定模型进行运动轨迹跟踪。针对该任务提出了一种I型ILC算法。此外，还探索了一个基于知识的运动学分析模型框架，以证明所提出的ILC的收敛性。最后，通过仿真和实验验证了ILC的有效性，结果表明软爬行机器人能够实现优异的跟踪性能。
