Implementation Of Neurofuzzy Controller To Robot Manipulator

Abstract

This paper presents a neurofuzzy controller that is applied to robotic manipulators. Robotic manipulators are highly nonlinear, coupled multivariable dynamical system, and may contain uncertain elements such as friction and load. Many efforts have been made in developing control schemes to achieve the precise tracking control of robot manipulators. For this reason, classical linear controller, such as PID (Proportional, Integral, and Derivative) controller, will provide robustness only over relatively small range operation because of complexity and nonlinearity of the system. Neurofuzzy employed in this system to increase the range operation without lack of robustness. Before considering the actual control system, a neurofuzzy controller must be trained. Two strategies of training are presented in this paper : generalized training and specialized training. In generalized training a neurofuzzy controller is trained off-line. The objective of this training is the controller should performed the ability to follow an input signal over the wide range operation even the transient response is poor. Specialized training is on-line procedure learning. Based on the result of generalized training a neurofuzzy controller is trained to achieve the desired transient response.The results proved the potency of the neurofuzzy in robotic manipulators control systems. Neurofuzzy control systems are essentially nonlinear systems, due to the nature of the nonlinear neurofuzzy controller. Mostly, the nonlinear system is so difficult to be solved. Consequently, the analysis of such systems is complicated, particularly, when a neurofuzzy controller is involved. This is because of the absence of a universal mathematical model.