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Modeling dynamic behavior of dielectric elastomer muscle for robotic applications

Recently, as a strong candidate for artificial muscle, dielectric elastomer actuators (DEAs) have been given the spotlight due to their attractive benefits from fast, large, and reversible electrically-controllable actuation in ultra-lightweight structures. Meanwhile, for practical use in mechanical...

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Detalles Bibliográficos
Autores principales: Jeong, Seung Mo, Mun, Heeju, Yun, Sungryul, Kyung, Ki-Uk
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9950498/
https://www.ncbi.nlm.nih.gov/pubmed/36845178
http://dx.doi.org/10.3389/fbioe.2023.1006346
Descripción
Sumario:Recently, as a strong candidate for artificial muscle, dielectric elastomer actuators (DEAs) have been given the spotlight due to their attractive benefits from fast, large, and reversible electrically-controllable actuation in ultra-lightweight structures. Meanwhile, for practical use in mechanical systems such as robotic manipulators, the DEAs are facing challenges in their non-linear response, time-varying strain, and low load-bearing capability due to their soft viscoelastic nature. Moreover, the presence of an interrelation among the time-varying viscoelasticity, dielectric, and conductive relaxations causes difficulty in the estimation of their actuation performance. Although a rolled configuration of a multilayer stack DEA opens up a promising route to enhance mechanical properties, the use of multiple electromechanical elements inevitably causes the estimation of the actuation response to be more complex. In this paper, together with widely used strategies to construct DE muscles, we introduce adoptable models that have been developed to estimate their electro-mechanical response. Moreover, we propose a new model that combines both non-linear and time-dependent energy-based modeling theories for predicting the long-term electro-mechanical dynamic response of the DE muscle. We verified that the model could accurately estimate the long-term dynamic response for as long as 20 min only with small errors as compared with experimental results. Finally, we present future perspectives and challenges with respect to the performance and modeling of the DE muscles for their practical use in various applications including robotics, haptics, and collaborative devices.