Dynamic Modeling and Real-Time Control of Continuum Soft Robots

Abstract

Continuum soft robots have attracted significant attention due to their high flexibility, compliance, and ability to perform complex tasks in unstructured environments. However, their inherent nonlinear, distributed-parameter dynamics pose major challenges for accurate modeling and real-time control. Existing approaches typically face a trade-off between computational efficiency and model fidelity, where simplified models lack accuracy, while high-fidelity formulations are computationally prohibitive for real-time implementation. This study addresses these limitations by proposing a hybrid framework for dynamic modeling and real-time control of continuum soft robots.
The proposed approach integrates a reduced-order dynamic model derived from continuum mechanics with a data-driven compensation module to enhance modeling accuracy while maintaining computational efficiency. The reduced-order formulation captures the dominant deformation behavior of the robot, while a learning-based component compensates for unmodeled dynamics and uncertainties. Based on this hybrid model, a real-time control strategy is developed to achieve accurate trajectory tracking under nonlinear and uncertain operating conditions.
Comprehensive evaluations are conducted through simulation-based studies and comparative analysis with state-of-the-art modeling and control methods. The results demonstrate that the proposed framework achieves improved accuracy, reduced tracking error, and significantly enhanced computational efficiency compared to conventional approaches such as constant curvature, Cosserat rod, and FEM-based models. This improvement is achieved because the reduced-order dynamic model preserves the dominant deformation characteristics while lowering computational complexity, and the data-driven compensation module corrects unmodeled dynamics and nonlinear effects that are not captured by conventional formulations. Consequently, the framework enables faster computation, more accurate state prediction, and more reliable real-time control performance. Furthermore, the proposed method maintains real-time feasibility while ensuring robustness against disturbances and noise.