Data-Driven Dynamic Modeling of a Tendon-Actuated Continuum Robot
机器人学
2026-05-19 v1
摘要
Developing dynamic models for tendon-driven continuum robots is challenging due to their nonlinear, high-dimensional, and friction-dominated dynamics. This paper presents a comparative study of data-driven system identification methods, including N4SID, ARX, and SINDYc, for modeling a tendon-actuated continuum robot with rolling joints developed at CERN. Despite the high number of joints of the robot, experimental analysis reveals that a two-degree-of-freedom dynamic model can accurately capture the system dynamics, owing to strong kinematic dependencies between the joints. The models are validated against experimental data, and used in the design of a model predictive controller, demonstrating their feasibility for real-time control.
引用
@article{arxiv.2605.18720,
title = {Data-Driven Dynamic Modeling of a Tendon-Actuated Continuum Robot},
author = {Harald Minde Hansen and Bjørn Kåre Sæbø and Kristin Y. Pettersen and Jan Tommy Gravdahl and Mario Di Castro},
journal= {arXiv preprint arXiv:2605.18720},
year = {2026}
}