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

TL;DR

This work tackles robust grasping force control for a lightweight cable-driven five-finger hand. It advances a novel framework that extends Youla-parameterization into a feedforward iterative learning control (ILC) by unifying the feedback and learning components under a single design parameter $Q(z)$. A systematic design process is demonstrated: system identification of the plant $P(z)$, selection of a target closed-loop transfer $G_c(z)$, and derivation of the controller $C(z)$ and learning filter $L(z)$ from $Q(z)$. Experimental results across multiple objects show that grasping force error decreases over 2–3 iterations, confirming effective adaptation while highlighting practical limits from sensing and actuation.

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.

Figures (8)

• Figure 1: Overview of IFRH wei2022novel
• Figure 2: Transmission of IFRH compared with a human hand wei2022novel ($\theta_{1,2,3}$: angles of EKC; $\Delta L$: pulled distance of PLA Band; $\theta'_{1,2,3}$: angles of human finger joint;$\Delta L'$: length changes of human finger during bending.)
• Figure 3: Grasping Adaptation Framework using ILC
• Figure 4: System Identification: Simulated Output Using Identified Model vs. Actual Experimental Output Data
• Figure 5: Bode Plots for the Controller and Plant