Applying RBF Neural Nets for Position Control of an Inter/Scara Robot

Abstract: This paper describes experimental results applying artificial neural net-works 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 conven-tional controller based on the feedback error learning architecture. The main advan-tage of this architecture is that it does not require any modification of the previous conventional controller algorithm. MLP and RBF neural networks trained on-line have been used, without requiring any previous knowledge about the system to be controlled. These approach has performed very successfully, with better results ob-tained with the RBF networks when compared to PID and sliding mode positional controllers