Abstract Tendon-driven actuation allows for light and compact manipulator designs with enhanced safety features. One of the key challenges in model-based control of tendon-driven robots is the increased complexity of the dynamic model, due in large part to difficult-to-model behavior like nonlinear dynamic deformation and friction at the tendons. While purely data-driven modeling approaches, e.g., neural networks, free one from dealing with complex and often error-prone mechanics models, they usually do not generalize well to diverse tasks, and also do not offer the needed intuitive understanding or predictive power of traditional mechanics-based models. In this paper, we present a hybrid modeling approach for complex tendon-driven robots, which effectively complement the limitations of pure physics-based and data-driven learning-based approaches. Rigid multibody equations of motion are augmented with (i) a configuration-dependent viscous-Coulomb friction model and (ii) a recurrent neural network that captures the tendon dynamics, and estimates link joint angles from the motor positions, velocities, and torques. Experiments involving a two-dof tendon-driven parallel wrist mechanism and the 7-dof AMBIDEX tendon-driven manipulator validate the performance advantages of our hybrid model-based control framework.

中文翻译：

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AMBIDEX 肌腱驱动机械手的混合动力学模型

摘要 肌腱驱动驱动允许具有增强安全功能的轻型和紧凑型机械手设计。肌腱驱动机器人基于模型的控制的关键挑战之一是动态模型的复杂性增加，这在很大程度上是由于难以建模的行为，如肌腱的非线性动态变形和摩擦。虽然纯粹的数据驱动建模方法，例如神经网络，使人们免于处理复杂且往往容易出错的力学模型，但它们通常不能很好地泛化到不同的任务，也不能提供所需的直观理解或预测能力传统的基于力学的模型。在本文中，我们提出了一种复杂的腱驱动机器人的混合建模方法，它有效地补充了纯物理和数据驱动的基于学习的方法的局限性。刚性多体运动方程增加了 (i) 依赖于配置的粘性库仑摩擦模型和 (ii) 一个循环神经网络，该网络捕获肌腱动力学，并根据电机位置、速度和扭矩估计链接关节角度。涉及两自由度肌腱驱动平行腕机构和 7 自由度 AMBIDEX 肌腱驱动机械手的实验验证了我们基于混合模型的控制框架的性能优势。