Abstarct: This dissertation deals with uncertainty estimation in robust tracking control of robot manipulators using voltage control strategy (VCS). In comparison with torque control strategy (TCS), VCS is simpler and less computational, since it does not need the robot dynamical model. According to the universal approximation theorem, neural networks and fuzzy systems can approximate nonlinear systems with arbitrary small approximation error. However, there are also other approximators such as Fourier series and Legendre polynomials. In this dissertation, these approximators are used in robust tracking control of robot manipulators. The most important advantage of these approximators in comparison with adaptive neuro-fuzzy systems is reducing the number of sensors. Fourier series expansion has been used in some previous related works. However, the suitable value for the fundamental period duration of Fourier series expansion has not been determined. This thesis addresses this issue and intuitively shows that in order to perform repetitive tasks, the least common multiple (LCM) of fundamental period durations of the desired trajectories of the joints is a proper value for the fundamental period duration of the Fourier series expansion. Selecting the LCM results in the least tracking error. Moreover, the truncation error is compensated by the proposed control law to make the tracking error as small as possible. Adaptation laws for determining the Fourier series coefficients are derived according to the stability analysis. Robust control in the task-space is more complicated due to the uncertainties in the Jacobian matrix. In this thesis, baxsed on the VCS, a conventional robust task-space controller is presented. Then, it is modified using Legendre polynomials for uncertainty estimation to reduce the number of sensors. Another novelty of this thesis is presenting a rigorous stability analysis for brain emotional learning control of uncertain nonlinear systems. The proposed controllers baxsed on the VCS in this thesis are expesimentally tested on a real SCARA robot driven by permanent magnet DC motors for the first time.