Fractional-order Modeling for Nonlinear Soft Actuators via Particle Swarm Optimization
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908724963049472 |
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| author | Yang, Wu-Te Tomizuka, Masayoshi |
| author_facet | Yang, Wu-Te Tomizuka, Masayoshi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_18213 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fractional-order Modeling for Nonlinear Soft Actuators via Particle Swarm Optimization Yang, Wu-Te Tomizuka, Masayoshi Robotics 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. |
| title | Fractional-order Modeling for Nonlinear Soft Actuators via Particle Swarm Optimization |
| topic | Robotics |
| url | https://arxiv.org/abs/2512.18213 |