Design Optimization and Data-driven Shallow Learning for Dynamic Modeling of a Smart Segmented Electroadhesive Clutch
Design Optimization and Data-driven Shallow Learning for Dynamic Modeling of a Smart Segmented Electroadhesive Clutch
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DOI：
10.1109/icra48891.2023.10161225
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发表时间：
2023-05
期刊：
影响因子：
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通讯作者：
N. Feizi;Zahra Bahrami;S. F. Atashzar;M. Kermani;Rajnikant V. Patel
中科院分区：
文献类型：
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作者：
N. Feizi;Zahra Bahrami;S. F. Atashzar;M. Kermani;Rajnikant V. Patel
在过去的十年里，电胶离合器由于其轻便、低功耗和可调的高扭矩输出能力，作为人与机器人交互的半主动执行器引起了人们的极大兴趣。然而，由于它们动力学的复杂性，在大多数情况下，它们被用于开/关控制策略。在这方面，电胶行为的非自主(依赖于时间)的退化是一个固有的挑战，为这类半主动离合器的行为注入了不可预测性和不确定性。我们提出了一种防止电粘着退化的新方法，该方法采用分段电极设计，在使用直流信号的同时调制介质表面的电场，并确保低功耗。本文首次提出了一种基于一种新的分析模型的优化过程。提出了一种基于混合浅层学习方法的数据驱动模型扩充方法，该方法由长短期记忆(LSTM)结构和分析模型相结合组成。本文对所提出的半主动离合器和数据驱动混合模型的性能进行了实验验证。
Electroadhesive clutches have attracted a great deal of interest in the last decade as semi-active actuators for human-robot interaction due to their lightweight, low power consumption, and tunable high-torque output capability. However, because of the complexity of their dynamics, in most cases, they are utilized in an ON/OFF -control strategy. In this regard, the non-autonomous (time-dependent) degradation of electroadhesive behavior is an inherent challenge that injects unpredictability and uncertainty into the behavior of this family of semi-active clutches. We propose a novel approach to preventing degradation of electroadhesion using a segmented electrode design that modulates the electrical field on the dielectric surface while using a direct current signal and securing low power consumption. This paper, for the first time, presents an optimization process based on a novel analytic model of the proposed actuator. It also develops a data-driven model augmentation using a hybrid shallow learning approach composed of a long short-term memory (LSTM) architecture which is combined with the analytical model. The performance of the proposed semi-active clutch and the data-driven hybrid model is experimentally validated in this paper.