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This paper considers the issue of precise control of robotic manipulators in the presence of dynamic uncertainties along with hard nonlinear perturbation such as friction using Modified Transpose Effective Jacobian and model based friction compensator. In order to model friction in robot joints, The LuGre friction model has been used and its unknown parameters have been identified by a bio-inspired optimization algorithm called Cuttlefish. By comparing Cuttlefish with other meta-heuristic algorithms such as Glowworm swarm optimization, its superiorities have been proved. After accurate identification of model parameters and determine frictions function, using Modified Transpose Effective Jacobian and model-based friction compensator, a two link planar manipulator has been controlled experimentally. Furthermore in order to compare the controller performance with other methods, the mentioned manipulator has been controlled using computed torque controller and transpose Jacobian besides Adaptive Neural Network Radial Based Function friction compensators. Experimental results offer the Modified Transpose effective Jacobian control method has privileges for better tracking control with more accuracy and better friction compensating as well as better robustness against dynamic uncertainties with lower computational efforts.