Fractional-order Modeling for Nonlinear Soft Actuators via Particle Swarm Optimization

TL;DR

This paper introduces a novel fractional-order modeling framework for soft pneumatic actuators, utilizing particle swarm optimization for parameter identification, resulting in more accurate and data-efficient models of complex nonlinear soft materials.

Contribution

The paper presents a new fractional-order differential equation approach combined with PSO for precise, data-driven modeling of nonlinear soft actuators, independent of material databases.

Findings

Enhanced modeling accuracy over traditional methods

Robust parameter estimation directly from experimental data

Improved predictive performance of soft actuator models

Abstract

Modeling soft pneumatic actuators with high precision remains a fundamental challenge due to their highly nonlinear and compliant characteristics. This paper proposes an innovative modeling framework based on fractional-order differential equations (FODEs) to accurately capture the dynamic behavior of soft materials. The unknown parameters within the fractional-order model are identified using particle swarm optimization (PSO), enabling parameter estimation directly from experimental data without reliance on pre-established material databases or empirical constitutive laws. The proposed approach effectively represents the complex deformation phenomena inherent in soft actuators. Experimental results validate the accuracy and robustness of the developed model, demonstrating improvement in predictive performance compared to conventional modeling techniques. The presented framework provides a data-efficient and database-independent solution for soft actuator modeling, advancing the precision and adaptability of soft robotic system design.