Physics-informed reduced-order modeling for real-time control of soft actuators

Abstract

The significant deformations and nonlinear characteristics of soft robots pose substantial challenges for accurate modeling. Although various dynamic modeling methods for soft actuators have been explored, existing approaches have excessively long computation times, making them unsuitable for real-time control of soft actuators. To address these issues, this paper proposes an efficient dynamic modeling method for soft actuators. The core idea is to ensure model accuracy by integrating moment-curvature equation with the Lagrangian equation. Additionally, the dynamic model is simplified using Taylor expansion to enhance computational efficiency without compromising control accuracy. The model also accounts for the actuator’s gravity and the buoyancy effects of water on its motion. To validate the effectiveness of our proposed model, we performed dynamic model verification experiments in a laboratory setting. The experimental results indicate that the model achieves an error rate of less than 9.23%, with computation times ranging from 0.0094 to 0.015 s. This approach offers a new solution for real-time control of soft actuators.

Keywords

Dynamic modeling / soft actuators / moment-curvature equation / Lagrangian equation / computational efficiency

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