Learning impedance control of manipulation robots by feedforward connectionist structures

A major objective in this paper is the application of new connectionist structures for fast and robust online learning of internal robot dynamic relations used as part of impedance control strategies in the case of robot contact tasks. Using proposed connectionist structures, stabilization of robot motion and interaction force with environment is achieved. The proposed neural network models with their special topology are integrated in position-based impedance control, force-based impedance control and stabilizing impedance control. In this way, efficient dynamic compensation and fast learning properties of the control algorithm for contact tasks are enabled. The effectiveness of the learning method is shown by simulation experiments of robot deburring process.