Data-Driven Dynamic Modeling of a Tendon-Actuated Continuum Robot
For robotics researchers, it demonstrates that low-order models suffice for complex continuum robots, reducing modeling complexity.
This paper shows that a two-degree-of-freedom dynamic model, identified via data-driven methods (N4SID, ARX, SINDYc), accurately captures the nonlinear dynamics of a high-joint-count tendon-driven continuum robot, enabling real-time model predictive control.
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.