Based on the orthogonal function theorem [18], LP and FS can approximate nonlinear functions. In other words, similar to neural networks and fuzzy systems, LP and FS are universal approximators. As a result, similar to direct adaptive fuzzy control in which fuzzy systems play the role of controller, LP and FS can be considered as controller in a direct adaptive scheme. Then, the Legendre coefficients or Fourier series coefficients can be tuned online using the adaptation laws derived from the stability analysis. In this study, this control idea is applied to position control of an electrically driven robot manipulator. As mentioned earlier, in comparison with neuro-fuzzy controllers, FAT-based controllers are simpler to tune. Moreover, in comparison with the previous FAT-based approaches [10–12], the proposed controller in this paper is simpler, since there is no need to consider an approximator for each element of matrices introducing the manipulator dynamics such as inertia matrix. To show the superiority of the proposed method, a comparison has been performed using a neuro-fuzzy control.