Simplifying Data-Driven Modeling of the Volume-Flow-Pressure Relationship in Hydraulic Soft Robotic Actuators: A Practical and Balanced Solution
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Updated: Nov 9, 2026

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Published on: August 17, 2018
Sang-Yoep Lee1, Leonardo Zamora Yañez1, Jacob Rogatinsky1
1Department of Mechanical Engineering, Boston University, Boston, MA 02215 USA.
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.
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