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

TL;DR

This paper compares data-driven system identification methods to develop an accurate, low-dimensional dynamic model of a tendon-actuated continuum robot, enabling real-time control via model predictive control.

Contribution

It demonstrates that a two-degree-of-freedom model suffices for complex tendon-driven robots, validated through experimental data and control implementation.

Findings

A two-DOF model accurately captures the robot's dynamics.

Data-driven methods like N4SID, ARX, and SINDYc are effective.

Model predictive control based on these models is feasible for real-time use.

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.