Learning impedance control for robotic manipulators

Learning control is a concept for controlling dynamic systems in an iterative manner. It arises from the recognition that robotic manipulators are usually used to perform repetitive tasks. Most researches on the iterative learning control of robots have been focused on the problem of free motion control and hybrid position/force control where the learning controllers are designed to track the desired motion and force trajectories. The iterative learning impedance control of robotic manipulators, however, has been studied recently. 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.