An Iterative Learning- Control Scheme for Impedance Control of Robotic Manipulators

In this paper, an iterative learning-control law is proposed for impedance control of robotic manipulators. In most of the learning- controller designs in the literature, a reference trajectory is given and a learning algorithm is designed to force the trajectory track ing error to converge to zero as the action is repeated. In contrast, our approach allows the performance of the learning system to be specified by a target impedance. A design method for analyzing the learning-impedance system is developed, and sufficient conditions for guaranteeing the convergence of the error to zero are derived. The robustness of the learning impedance-control system to the fluc tuation of the dynamics, output measurement noise, and error in the initial conditions is also analyzed in details. Experimental results on a system using an industrial robot (SEIKO TT3000) are presented to illustrate the theoretical results.