# Data-driven modeling and identification of a bistable soft-robot element based on dielectric elastomer

## TL;DR

This paper introduces a hybrid modeling approach for a bistable soft robot using dielectric elastomer actuators, combining physics-based and data-driven methods.

## Contribution

The novel contribution is a hybrid framework combining physics-based and data-driven RBF modeling for bistable soft robots.

## Key findings

- A physics-based model was developed to describe electromechanical coupling and dynamic behavior.
- RBF networks were used to correct model discrepancies from asymmetries and unmodeled effects.
- The hybrid model was validated through analytical, numerical, and experimental methods.

## Abstract

This paper presents the development and experimental validation of a hybrid modeling framework for a bistable soft robotic system driven by dielectric elastomer (DE) actuators. The proposed approach combines physics-based analytical modeling with data-driven radial basis function (RBF) networks to capture the nonlinear and dynamic behavior of the soft robots. The bistable DE system consists of a buckled beam structure and symmetric DE membranes to achieve rapid switching between two stable states. A physics-based model is first derived to describe the electromechanical coupling, energy functions, and dynamic behavior of the actuator. To address discrepancies between the analytical model and experimental data caused by geometric asymmetries and unmodeled effects, the model is augmented with RBF networks. The model is refined using experimental data and validated through analytical, numerical, and experimental investigation.