Design Optimization and Data-driven Shallow Learning for Dynamic Modeling of a Smart Segmented Electroadhesive Clutch
Feizi N, Bahrami Z, Atashzar SF, Kermani MR, Patel RV (2023)
Publication Type: Conference contribution
Publication year: 2023
Journal
Publisher: Institute of Electrical and Electronics Engineers Inc.
Book Volume: 2023-May
Pages Range: 9903-9909
Conference Proceedings Title: Proceedings - IEEE International Conference on Robotics and Automation
Event location: London, GBR
ISBN: 9798350323658
DOI: 10.1109/ICRA48891.2023.10161225
Abstract
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.
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APA:
Feizi, N., Bahrami, Z., Atashzar, S.F., Kermani, M.R., & Patel, R.V. (2023). Design Optimization and Data-driven Shallow Learning for Dynamic Modeling of a Smart Segmented Electroadhesive Clutch. In Proceedings - IEEE International Conference on Robotics and Automation (pp. 9903-9909). London, GBR: Institute of Electrical and Electronics Engineers Inc..
MLA:
Feizi, Navid, et al. "Design Optimization and Data-driven Shallow Learning for Dynamic Modeling of a Smart Segmented Electroadhesive Clutch." Proceedings of the 2023 IEEE International Conference on Robotics and Automation, ICRA 2023, London, GBR Institute of Electrical and Electronics Engineers Inc., 2023. 9903-9909.
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