Impedance control with on-line neural-network compensator for robot contact tasks

In this paper, a force-tracking impedance controller with an on-line neural-network compensator is shown to be able to track a reference force in the presence of unknown environmental dynamics. The controller can be partitioned into three parts. The computed torque method is used to linearize and decouple the dynamics of a manipulator. An impedance controller is then added to regulate the mechanical impedance between the manipulator and its environment. In order to track a reference force signal, an on-line neural network is used to compensate the effect of unknown parameters of the manipulator and environment.