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.

翻译：为肌腱驱动的连续体机器人开发动态模型具有挑战性，因其非线性、高维度和以摩擦为主的动力学特性。本文提出了一项关于数据驱动系统辨识方法的比较研究，包括N4SID、ARX和SINDYc，用于对CERN开发的一款具有滚动关节的肌腱驱动连续体机器人进行建模。尽管该机器人关节数量众多，但实验分析表明，由于关节之间存在强运动学依赖关系，一个二自由度动态模型便能够准确捕捉系统动力学特性。这些模型通过实验数据进行了验证，并用于模型预测控制器的设计，证明了其在实际控制中的可行性。