Abstarct: The following thesis scrutinizes the problem of optimal sliding mode control of a robot manipulator under uncertainty. Minimizing the control signal and tracking error are two of the most important goals in control systems, which should be taken into consideration. The novelty of this research is to combine the results of PSO algorithm in an offline mode with the estimated values of uncertainties in order to optimize the control signal parameters. The major advantage of the proposed method over other traditional ones is its online nature. In an online mode, one can have a better estimation of actual model if better bounds of uncertainty would be available. Therefore, the Particle Swarm Optimization technique will be applicable for the actual dynamical system. Another merit of the proposed method is its fewer online computations. This concerning the fact that majority of online computations would be already done in an offline mode and available in a table for the controller. A case study for PUMA is performed. This Robot is nonlinear, multi input-multi output systems with uncertain parameters, which should be control via Robust Control approaches such as Sliding Mode Control. The biggest barrier in the way of optimization of these systems is the existence of disturbances and uncertainties which varies the actual system from the nominal one. This study is taking advantage of both online estimation of uncertainty and a gain table optimized by PSO in an offline mode in order to improve the control signal parameters. Simulation results show improvement and the superiority of the proposed method over the traditional Sliding Mode Control approach.