Learning approximation of feedforward dependence on the task parameters: Experiments in direct-drive manipulator tracking

Abstract

This paper describes an experimental implementation of a novel paradigm for a model-free design of the trajectory tracking controller. The design is based on a nonlinear approximation of the feedforward dependence on control task parameters. These task parameters comprise initial and final set points of the system and define the trajectory to be tracked. As an approximation method, we use a radial basis function network. The initial feedforward data for the approximation are obtained by performing learning control iterations for a number of selected task parameter values. In our experiments with a direct-drive industrial robot AdeptOne, high performance of the designed approximation-based controller is achieved despite strongly nonlinear system dynamics and large Coulomb-friction. The obtained results open an avenue for industrial applications of the developed approach in robotics and elsewhere.