Abstarct: The dynamic equations of robot show that the robot's model is highly nonlinear, heavily coupled, multivariable and computationally extensive. In addition, the state space model of the robotic system including the robot manipulator and motors is in a non companion form, however, can be converted to the companion form by many calculations. Although, adaptive and robust control methods are suggested to overcome the uncertainty, they face difficulties because of the complexity of the dynamics of the robot. In contrast, designing fuzzy controllers due to being free of robot model seems to be easy. Thus, guaranteeing the stability of the fuzzy systems is difficult. Since an unstable control system is useless, it is necessary that the controller guarantees stability. A fuzzy system can be used as a universal approximator for any nonlinear system. This feature has been efficiently used to design the adaptive fuzzy controllers. Adaptive fuzzy control systems are designed baxsed on guaranteeing stability. Though, adaptive fuzzy control methods are very complex, computational, multivariable and they need all system states. Most of these problems are caused because the robot control methods are baxsed on torque control strategy. This thesis proposes a novel adaptive fuzzy robot control baxsed on voltage control strategy. Comparing to classic control methods, it is simpler, less computational and more applicable, it also guarantees stability. In this thesis, robot kinematic and dynamic modeling are presented and then, available robot control strategies are introduced. Next, Indirect and direct adaptive fuzzy controls using torque and voltage control strategies are designed and analysed. Then, they are simulated on an SCARA robot. Tracking and set-point performance in the presence of uncertainty is evaluated. Stability of the control systems is analyzed. Structure and the performance of the controllers are compared. This research shows adaptive fuzzy control using voltage control strategy is superior to torque control strategy.