Abstarct: In this thesis, adaptive dynamic sliding mode control for robotic systems using voltage control strategy baxsed on the Fourier series expansion is presented. Dynamic sliding mode is an effective approach for reducing the chattering phenomenon in traditional sliding mode control. However, the upper bound of uncertainty is required in dynamic sliding mode control. In order to solve this problem and also reduce the chattering phenomenon, an adaptive dynamic sliding mode control is designed in this thesis. Instead of using the sign function in dynamic sliding mode control, estimation of uncertainties baxsed on the Fourier series expansion is used in the control law of the adaptive dynamic sliding mode control. The novelty of this thesis is proposing a positive exponential function for the convergence rate of the adaptation rules with the aim of prevention from actuator saturation. Moreover, in order to overcome uncertainty, a continuous robust control term is proposed in the control law while in previous related works a discontinuous term was used. Using Lyapunov stability theorem and Barbalat’s lemma, it is guaranteed that the tracking error converges to zero asymptotically. The Fourier series estimator is superior in comparison with the fuzzy and Legendre estimators. In the proposed method, the fundamental frequency of the Fourier series is constant which is tuned using the trial and error procedure. Another novelty of this thesis is presenting an adaptive Fourier series in which the fundamental frequency is tuned using an adaptive rule. The Fourier series coefficients are regulated baxsed on the adaptation rules obtained from the stability analysis. The stability of the closed-loop system is verified using Lyapunov stability theorem and Barbalat’s lemma. Simulation results show the satisfactory performance of the proposed methods on the SCARA robot manipulator