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

2026-05-18

AI Summary

This work addresses the challenge of modeling tendon-driven continuum robots, whose dynamics are highly nonlinear, high-dimensional, and dominated by friction. To overcome these difficulties, a data-driven system identification approach is proposed, integrating the N4SID, ARX, and SINDYc algorithms to construct a low-dimensional dynamical model. The study reveals that the dynamic behavior of multi-segment continuum robots can be accurately captured by a two-degree-of-freedom model, uncovering strong kinematic coupling among joints and substantially reducing modeling complexity. Experimental validation demonstrates that the resulting model achieves high prediction accuracy and has been successfully implemented within a real-time model predictive controller, confirming its effectiveness and practical utility.

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.

Problem

• continuum robot
• tendon-actuated
• dynamic modeling
• nonlinear dynamics
• system identification

Innovation

• data-driven modeling
• continuum robot
• system identification
• model predictive control
• tendon-actuated