Artificial Control of PUMA Robot Manipulator: A-Review of Fuzzy Inference Engine And Application to Classical Controller.

Abstract

One of the most important challenges in the field of robotics is robot manipulators control withacceptable performance, because these systems are multi-input multi-output (MIMO), nonlinearand uncertainty. Presently, robot manipulators are used in different (unknown and/orunstructured) situation consequently caused to provide complicated systems, as a result strongmathematical theory are used in new control methodologies to design nonlinear robust controllerwith acceptable performance (e.g., minimum error, good trajectory, disturbance rejection).Classical and non-classical methods are two main categories of robot manipulators control,where the conventional (classical) control theory uses the classical method and the non-classicalcontrol theory (e.g., fuzzy logic, neural network, and neuro fuzzy) uses the artificial intelligencemethods. However both of conventional and artificial intelligence theories have applied effectivelyin many areas, but these methods also have some limitations. This paper is focused on review offuzzy logic controller and applied to PUMA robot manipulator.