Intelligent stabilization of direct drive manipulators

Abstract

This paper investigates the control of nonlinear systems by neural networks. As the control methods; intelligent PD controller is studied and the performances of both classical PD and intelligent PD controller are comparatively evaluated on a two degrees of freedom direct drive robotic manipulator with respect to trajectory tracking performance, computational complexity, design complexity, RMS errors, necessary training time in learning phase and payload variations.