Automatic Design of Soft Dielectric Elastomer Actuators With Optimal Spatial Electric Fields

Feifei Chen, Kun Liu, Yiqiang Wang, Jiang Zou, Guoying Gu, Xiangyang Zhu

Research output: Contribution to journalJournal articleResearchpeer-review

114 Downloads (Pure)


Dielectric elastomer actuators (DEAs) are a promising actuation technology in soft robotics owing to their large voltage-induced deformation and rapid response. However, most existing DEA design paradigms are empirical or intuitive, lacking the mathematical modeling and optimization methodology to exploit their actuation capabilities for prescribed motion tasks. In this paper, we present an automatic design methodology to maximize the concerned displacement(s) of DEAs by topology optimization of the applied spatial electric fields (SEFs). Our method is enabled by integrating the freeform SEF profile captured by implicit level sets, and the constitutive model of DEAs incorporating geometric and material nonlinearities and the electromechanical coupling effect, into a gradient-based optimizer. We implement our method for motions of single and multiple degrees of freedom (DOFs) of planar DEAs, and the optimized SEFs have been found to improve the output displacements by more than 75% compared with their intuitive counterparts. We further demonstrate a proof-of-concept application in which our designed two-DOF DEAs can actively drive various host structures to shape-morph from flat sheets to desired three dimensional configurations. Overall, our paper represents the first step toward automatic design of soft DEAs for diverse potential applications in soft machines and robots.
Original languageEnglish
JournalIEEE Transactions on Robotics
Issue number5
Pages (from-to)1150-1165
Publication statusPublished - 2019


  • Dielectric elastomer actuators (DEAs)
  • Level sets
  • Soft robotics
  • Topology optimization

Fingerprint Dive into the research topics of 'Automatic Design of Soft Dielectric Elastomer Actuators With Optimal Spatial Electric Fields'. Together they form a unique fingerprint.

Cite this