Modeling, Control and Self-sensing of Dielectric Elastomer Soft Actuators: A Review
For researchers in soft robotics, this review consolidates progress on DEA challenges, but it is incremental as it summarizes existing work without presenting new results.
This review surveys modeling, control, and self-sensing methods for dielectric elastomer actuators (DEAs), which face challenges like nonlinear elasticity and viscoelastic creep. It categorizes approaches into physics-based and phenomenological modeling, open-loop/feedback/adaptive control, and physics-based/data-driven self-sensing, highlighting remaining problems and opportunities.
Dielectric elastomer actuators (DEAs) have garnered extensive attention especially in soft robotic applications over the past few decades owing to the advantages of lightweight, large strain, fast response and high energy density. However, because the DEAs suffer from nonlinear elasticity, inherent viscoelastic creep, hysteresis and vibrational dynamics, the modeling, control and self-sensing of DEAs are challenging, thereby hindering the practical applications of DEAs. In order to address these challenges, numerous studies have been conducted. In this review, various physics-based modeling methods and phenomenological modeling methods for predicting the electromechanical response of DEAs are presented and discussed. Different control methods for DEAs are reviewed, which are classified into open-loop feedforward control, feedback control, feedforward-feedback control and adaptive feedforward control. Physics-based self-sensing methods and data-driven self-sensing methods for reconstructing the DEA displacement without the need for additional sensors are discussed. Finally, the existing problems and new opportunities for the further studies are summarized.