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arxiv: 2604.17199 · v1 · submitted 2026-04-19 · 💻 cs.RO · cs.SY· eess.SY

Modeling, Control and Self-sensing of Dielectric Elastomer Soft Actuators: A Review

Y. Zhao , G. Meng This is my paper

Pith reviewed 2026-05-10 06:37 UTC · model grok-4.3

classification 💻 cs.RO cs.SYeess.SY
keywords dielectric elastomer actuatorsmodeling methodscontrol strategiesself-sensingsoft roboticselectromechanical responseviscoelastic creephysteresis
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The pith

Review shows how to model, control, and self-sense dielectric elastomer actuators to overcome their nonlinear challenges.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This review presents physics-based and phenomenological modeling methods for predicting the electromechanical response of dielectric elastomer actuators. It classifies control methods into open-loop feedforward, feedback, feedforward-feedback, and adaptive feedforward categories. Physics-based and data-driven self-sensing methods are discussed for reconstructing displacement without extra sensors. A sympathetic reader would care because DEAs offer lightweight construction, large strain, fast response, and high energy density for soft robotics, yet nonlinear elasticity, viscoelastic creep, hysteresis, and vibrational dynamics have hindered practical use. The paper summarizes existing problems and outlines new research opportunities.

Core claim

The authors review that physics-based and phenomenological modeling methods can predict DEA electromechanical responses, control methods fall into open-loop feedforward, feedback, feedforward-feedback, and adaptive feedforward categories, and self-sensing can be achieved through physics-based or data-driven approaches without additional sensors, with existing problems and opportunities summarized at the end.

What carries the argument

The classification system that divides modeling into physics-based versus phenomenological approaches, control into four explicit strategy types, and self-sensing into physics-based versus data-driven categories.

If this is right

  • The reviewed modeling methods enable prediction of DEA deformation under electric fields while accounting for viscoelastic effects.
  • Control methods from the four categories can be selected to mitigate hysteresis and vibrational dynamics during operation.
  • Self-sensing techniques allow displacement reconstruction from electrical signals or learned patterns alone.
  • Addressing the summarized problems points toward more reliable DEA use in soft robotic applications.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Combining adaptive control with data-driven self-sensing could improve robustness in changing environments beyond the cases explicitly covered.
  • The same classification approach might apply to other soft actuators sharing similar nonlinear properties.
  • Experimental tests of the reviewed methods in integrated robotic systems would be a direct extension.

Load-bearing premise

The assumption that the selected studies comprehensively represent the state of the art on nonlinear elasticity, viscoelastic creep, hysteresis, and vibrational dynamics without selection bias or omission of key limitations.

What would settle it

Discovery of a significant modeling, control, or self-sensing technique for DEAs that is absent from the review, or an experiment demonstrating that a reviewed method fails to account for the listed nonlinear behaviors, would challenge the review's completeness.

Figures

Figures reproduced from arXiv: 2604.17199 by G. Meng, Y. Zhao.

Figure 1
Figure 1. Figure 1: Overview of modeling, control and self-sensing of DEAs. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: (a) Block diagram of the experimental setup for testing and controlling a DEA. (b) DE actuation principle. [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Various DEAs and DEA-driven devices having been controlled: (a) A robotic jaw driven by two planar DEAs [91]; (b) A rolled DEA [103]; (c) A [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Step response of a DEA [37] [PITH_FULL_IMAGE:figures/full_fig_p003_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Hysteresis loops in a DEA under harmonic excitations of different frequencies [41]. [PITH_FULL_IMAGE:figures/full_fig_p004_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: (a) Step response and (b) frequency response of DEAs based on the combinations of elastomers with varying [PITH_FULL_IMAGE:figures/full_fig_p004_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Different submodels and their relations in physics-based modeling of DEAs. [PITH_FULL_IMAGE:figures/full_fig_p005_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Equivalent circuit models of a DEA [PITH_FULL_IMAGE:figures/full_fig_p005_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Viscoelastic models for DEs: (a) three-parameter Voigt-Kelvin model [61]; (b) five-parameter Voigt-Kelvin model [36]; (c) a two- [PITH_FULL_IMAGE:figures/full_fig_p005_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Block diagram of the model structures for DEAs. [PITH_FULL_IMAGE:figures/full_fig_p006_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Block diagram of the open-loop control for DEAs. [PITH_FULL_IMAGE:figures/full_fig_p008_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Different types of control approaches for DEAs ( [PITH_FULL_IMAGE:figures/full_fig_p008_12.png] view at source ↗
Figure 14
Figure 14. Figure 14: Schematic of the control method based on deep reinforcement learning [PITH_FULL_IMAGE:figures/full_fig_p009_14.png] view at source ↗
Figure 13
Figure 13. Figure 13: Model-based feedback control schemes for control of soft DEAs. Adapted from [98], [106], [65], [108], [109] [PITH_FULL_IMAGE:figures/full_fig_p009_13.png] view at source ↗
Figure 15
Figure 15. Figure 15: The first type of feedforward-feedback control methods for soft DEAs. FFRLS: forgetting factor recursive least squares [PITH_FULL_IMAGE:figures/full_fig_p010_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: The second type of feedforward-feedback control methods for soft DEAs [PITH_FULL_IMAGE:figures/full_fig_p011_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Different adaptive control schemes for DEAs. [PITH_FULL_IMAGE:figures/full_fig_p012_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Self-sensing approaches for DEAs [PITH_FULL_IMAGE:figures/full_fig_p014_18.png] view at source ↗
read the original abstract

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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 2 minor

Summary. The manuscript is a review of dielectric elastomer actuators (DEAs) for soft robotics. It states that nonlinear elasticity, viscoelastic creep, hysteresis, and vibrational dynamics make modeling, control, and self-sensing challenging. The review presents physics-based and phenomenological modeling methods for electromechanical response, classifies control methods into open-loop feedforward, feedback, feedforward-feedback, and adaptive feedforward categories, discusses physics-based and data-driven self-sensing methods for displacement reconstruction without extra sensors, and summarizes existing problems plus future opportunities.

Significance. If the coverage is representative, the review would offer a structured reference for the soft-robotics community by organizing literature around the four stated challenges and by providing explicit credit to DEA advantages (lightweight, large strain, fast response, high energy density). The manuscript follows its announced outline with dedicated sections on modeling, control, and self-sensing, which is a strength for a survey paper.

minor comments (2)
  1. [Abstract] The abstract and introduction list the four challenges but do not indicate the total number of papers surveyed or the time window of the literature search; adding this information would help readers gauge completeness.
  2. [Figures and Tables] Figure captions and table headings should be checked for consistency with the text (e.g., whether all cited modeling approaches appear in a summary table).

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive evaluation of our review on modeling, control, and self-sensing of dielectric elastomer actuators. We appreciate the recognition that the manuscript follows its announced outline and organizes the literature around the key challenges of nonlinear elasticity, viscoelasticity, hysteresis, and dynamics. The recommendation for minor revision is noted, and we will address any editorial or minor clarifications in the revised version.

Circularity Check

0 steps flagged

No significant circularity: review paper with no internal derivations

full rationale

This is a literature review summarizing physics-based and phenomenological modeling methods, control strategies (open-loop, feedback, etc.), and self-sensing approaches for dielectric elastomer actuators, drawing exclusively from cited prior studies. No new equations, predictions, fitted parameters, or derivations are introduced by the authors themselves. The central claims are descriptive classifications and summaries of existing work, with no load-bearing steps that reduce to self-definition, fitted inputs renamed as predictions, or self-citation chains. All content is externally referenced, making internal circularity impossible by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is a review paper. It introduces no new free parameters, axioms, or invented entities; all content is drawn from cited prior studies.

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Reference graph

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