  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.
