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arXiv · 2605.18720

Data-Driven Dynamic Modeling of a Tendon-Actuated Continuum Robot

Harald Minde Hansen·Bjørn Kåre Sæbø·Kristin Y. Pettersen·Jan Tommy Gravdahl·Mario Di Castro
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Abstract

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.

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Harald Minde Hansen, Bjørn Kåre Sæbø, Kristin Y. Pettersen, Jan Tommy Gravdahl, Mario Di Castro. 2026-05-18. Data-Driven Dynamic Modeling of a Tendon-Actuated Continuum Robot. https://arxiv.org/abs/2605.18720

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