Abstarct: So far, the control of robot manipulators in joint-space has been extensively developed. The industrial robots show high accuracy, good resolution, and suitable repeatability, although, to achieve a high grade of quality, the cost of constructing will be increased. Alternatively, to save the cost we can improve the performance of robot by the use of enhanced controllers to overcome uncertainties. The significance of robot control is due to the two facts, 1- dynamics of robot manipulator is highly nonlinear with coupling between joints and variations in inertia and load. Such the dynamical effects will be dominant as the velocity is increased. Therefore, such a complex system cannot be easily described by a precise mathematical model. Consequently, the use of model-baxsed controllers will be affected by these uncertainties. 2- The perfect tracking of robot manipulators in joint space cannot guarantee the perfect tracking of the end-effecter in workspace under uncertainties. Thus, in this thesis we propose an adaptive fuzzy control of robot manipulators in task space. Human knowledge about the plant dynamics and control strategies can be incorporated into adaptive fuzzy controllers, whereas such knowledge is not considered in conventional adaptive control systems. This identifies the main advantage of adaptive fuzzy control over conventional adaptive control. So, in this thesis, two different methods of adaptive fuzzy control are implemented on Puma560 robot. The first one is a new adaptive fuzzy control by variable scaling factors. The second method is a typical adaptive fuzzy control named direct adaptive fuzzy. These two methods are simulated and steady state error and robustness of both are compared. Then, by the inverse jacobian strategy, these methods are used in task space. The simulation results show the adaptive fuzzy controller efficiency in robotic control systems in both joint space and task space.