Simplifying Data-Driven Modeling of the Volume-Flow-Pressure Relationship in Hydraulic Soft Robotic Actuators

TL;DR

This paper presents a data-driven approach using simple polynomial models to accurately predict the volume-flow-pressure relationship in hydraulic soft robotic actuators, enabling real-time control with reduced complexity.

Contribution

It introduces low-complexity, high-accuracy polynomial models for soft actuator dynamics, improving over traditional physics-based models.

Findings

Multivariate polynomial models effectively predict pressure dynamics.

Simpler models require fewer parameters and computational resources.

Approach suitable for real-time soft robotics applications.

Abstract

Soft robotic systems are known for their flexibility and adaptability, but traditional physics-based models struggle to capture their complex, nonlinear behaviors. This study explores a data-driven approach to modeling the volume-flow-pressure relationship in hydraulic soft actuators, focusing on low-complexity models with high accuracy. We perform regression analysis on a stacked balloon actuator system using exponential, polynomial, and neural network models with or without autoregressive inputs. The results demonstrate that simpler models, particularly multivariate polynomials, effectively predict pressure dynamics with fewer parameters. This research offers a practical solution for real-time soft robotics applications, balancing model complexity and computational efficiency. Moreover, the approach may benefit various techniques that require explicit analytical models.