Intelligent stabilization of direct drive manipulators

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.