Learning-while controlling RBF-NN for robot dynamics approximation in neuro-inspired control of switched nonlinear systems

Abstract

Radial Basis Function-Neural Networks are well-established function approximators. This paper presents an adaptive Gaussian RBF-NN with an extended learning-while controlling behaviour. The weights, function centres and widths are updated online based on a sliding mode control element. In this way, the need for fixing parameters a priori is overcome and the network is able to adapt to dynamically changing systems. The aim of this work is to present an extended adaptive neuro-controller for trajectory tracking of serial robots with unknown dynamics. The adaptive RBF-NN is used to approximate the unknown robot manipulator dynamics-function. It is combined with a conventional controller and a bio-inspired extension for the control of a robot in the presence of switching constraints and discontinuous inputs. The controller-extension increases the robustness and adaptability of the system. Its learned goal-directed output results from the complementary action of an actuator, A, and a preventer, P. The trigger is an incentive, I, based on the weighted perception of the environment. The concept is validated through simulations and implementation on a KUKA LWR4-robot.