Abstract
Impedance control of robotic manipulators allows them to perform interaction task where safety is the foremost requirement. A major hurdle in the implementation of impedance control has always been the requirement of availability and online real-time computation of dynamic model. This paper presents a complete data-driven, machine learning approach to impedance control in real time of an industrial manipulator. The technique used here to learn the inverse dynamic model of an industrial robot is based on an incremental nonparametric statistical learning approach and is called locally weighted projection regression. Unlike traditional model-based control schemes, the proposed control strategy requires less a priori knowledge of the system being modeled, is computationally efficient to run in hard real time, and can be updated during online operation of the arm. The main contribution of this paper is the development of learning-based impedance control scheme and its implementation in hard real time (at 500 Hz control loop rate) on an industrial robot (Barrett WAM arm). To validate performance of the proposed scheme, its comparison with a controller based on an analytically derived model (computed online in a numeric recursive way using Newton–Euler approach) has also been presented through experimentation in a laboratory environment.
Published Version
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