Learning impedance control for robotic manipulators

Abstract

In this paper, an iterative learning impedance control problem for robotic manipulators is formulated and solved. A target impedance is specified and a learning controller is designed such that the system follows the desired response specified by the target model as the actions are repeated. A design method for analyzing the convergence of the learning impedance system is developed. A sufficient condition for guaranteeing the convergence of the system is also derived. The proposed learning impedance control scheme is implemented on an industrial selective compliance assembly robot arm (SCARA) robot, SEIKO TT3000. Experimental results verify the theory and confirm the effectiveness of the learning impedance controller.