Work

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

Year: 2026

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2605.18720

Authors: Hansen, Harald Minde, Sæbø, Bjørn Kåre, Pettersen, Kristin Y., Gravdahl, Jan Tommy, Di Castro, Mario

Institutions: Norwegian University of Science and Technology, European Organization for Nuclear Research

Language: English

Topic: Soft Robotics and Applications

Subfield: Biomedical Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funder: European Commission

OpenAlex ID: W7161712281

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