Grasping Force Control and Adaptation for a Cable-Driven Robotic Hand

TL;DR

This paper presents a novel, easy-to-implement force control and adaptation algorithm for a lightweight five-fingered robotic hand, extending Youla-parameterization into a feedforward ILC for improved grasping performance.

Contribution

It introduces a unified parameterization of feedback and feedforward controllers using Youla-parameterization extended to ILC, enhancing robustness and adaptability in robotic hand control.

Findings

Effective grasping demonstrated through experiments

Unified controller parameterization simplifies design

Improved robustness and adaptability in control performance

Abstract

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.