绳驱机械手的抓取力控制与自适应
Grasping Force Control and Adaptation for a Cable-Driven Robotic Hand
AI总结：
本文为轻量级五指机械手IFRH提出了一种基于Youla参数化扩展的力控制与前馈自适应算法，通过统一参数设计实现了直观且易于实现的鲁棒自适应学习能力，并经实验验证有效。
摘要：
本文为一种轻量级、低复杂度的五指机械手，即集成手指机械手（IFRH），提出了一种独特的力控制与自适应算法。该力控制与自适应算法设计直观、易于实现，并通过前馈自适应自动提升了抓取功能。具体而言，我们将传统上用于反馈控制器设计的Youla参数化扩展为一种前馈迭代学习控制算法（ILC）。这种扩展的独特之处在于，反馈和前馈控制器均基于一个统一的设计参数进行参数化，该参数可根据期望的闭环性能轻松定制。虽然Youla参数化和ILC过去曾在各种应用中被探索过，但我们独特的参数化和计算方法使设计直观且易于实现。这提供了鲁棒且自适应的学习能力，且我们的应用可与许多机械手控制系统的复杂性相媲美。我们进行了广泛的实验测试以验证该方法的有效性。
英文摘要：
This paper introduces a unique force control and adaptation algorithm for a lightweight and low-complexity five-fingered robotic hand, namely an Integrated-Finger Robotic Hand (IFRH). The force control and adaptation algorithm is intuitive to design, easy to implement, and improves the grasping functionality through feedforward adaptation automatically. Specifically, we have extended Youla-parameterization which is traditionally used in feedback controller design into a feedforward iterative learning control algorithm (ILC). The uniqueness of such an extension is that both the feedback and feedforward controllers are parameterized over one unified design parameter which can be easily customized based on the desired closed-loop performance. While Youla-parameterization and ILC have been explored in the past on various applications, our unique parameterization and computational methods make the design intuitive and easy to implement. This provides both robust and adaptive learning capabilities, and our application rivals the complexity of many robotic hand control systems. Extensive experimental tests have been conducted to validate the effectiveness of our method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
