Parallel adaptive neural network control of robots

Abstract

In this paper, a parallel adaptive neural network (NN) control design for robots motivated by the work by Lee and Tan is presented. The controller is based on direct adaptive techniques and an approach of using an additional parallel NN to provide adaptive enhancements to a basic fixed controller, which can be either a NN-based non-linear controller or a model-based non-linear controller. It is shown that, if Gaussian radial basis function networks are used for the additional parallel NN, uniformly stable adaptation is assured and asymptotic tracking of the position reference signal is achieved.