Abstarct: The backstepping control is a Lyapunov-baxsed systematic recursive method in which the stability of the system is proven by the stabilizing the subsystems sequently. In this thesis, a tracking adaptive fuzzy control with a new point of view is considered in the frxamework of the backstepping method and the challenges of design are studied. As a case study, the proposed controller laws are applied to electrically-driven robotic manipulators. First, a suitable strict-feedback form is extracted for applying the backstepping method and the tracking purpose is achieved accordingly. Then, an adaptive fuzzy control baxsed on the backstepping method is proposed considering the input delay and partial state constraints using barrier Lyapunov functions. The fuzzy systems are used to approximate unknown terms. It is worth noting that the coupling between system dynamics and uncertainties is improved with the aid of the Young inequality. To reduce the complexity explosion and increasing the accuracy of fuzzy approximation in the presence of input delay, a new composite fuzzy learning control baxsed on backstepping method law is proposed for the system in which the disturbance observer is used to enhance the system performance. Following the proposed composite learning controller, a new prespecified adaptive fuzzy controller is developed to study the performance indices of the system response in the frxamework of the backstepping method. The stability of the closed-loop system is proven by the Lyapunov theorem throughout the thesis. Simulation results emphasize the effectiveness of the proposed control laws in comparison with the previous related research.