用数据驱动方法建模腱驱连续机器人，仅两自由度即可精准捕捉动态特性。
Data-Driven Dynamic Modeling of a Tendon-Actuated Continuum Robot
- 采用N4SID、ARX、SINDYc等数据驱动方法识别系统动态
- 实验表明两自由度模型即可准确描述高自由度机器人的运动
- 模型用于模型预测控制，适用于实时控制场景
由于腱驱连续机器人具有非线性、高维和摩擦主导的动态特性，其动力学建模极具挑战。本文针对在CERN开发的带滚动关节的腱驱连续机器人，对比研究了N4SID、ARX和SINDYc等数据驱动系统辨识方法。尽管该机器人自由度数量较高，但实验分析显示，由于关节间存在强运动学耦合，仅需两自由度动态模型即可准确捕捉系统动态。模型经实验数据验证，并用于模型预测控制器设计，证明其在实时控制中的可行性。
原文摘要 · Abstract (English)
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.
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