Feedback error learning neural network applied to a scara robot

Abstract

This paper describes experimental results applying artificial neural networks to perform the position control of a real scara manipulator robot. The general control strategy consists of a neural controller that operates in parallel with a conventional controller based on the feedback error learning architecture. The main advantage of this architecture is that it does not require any modification of the previous conventional controller algorithm. MLP and RBF neural networks trained online have been used, without requiring any previous knowledge about the system to be controlled. The approach has performed very successfully, with better results obtained with the RBF networks when compared to PID and sliding mode positional controllers.