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Uncalibrated, model free, robot visual servoing has been widely applicable in robot vision due to minimal requirements related to calibration and robot kinematic’ s parameters. The numerical quasy-Newton methods offer a theoretical background for problem solving, which has been proven hard as the real system has been influenced with the noise. Consequently, additional attention has to be paid which assured stability and the robustness of the proposed method. In this paper we have introduced a new, modified Broyden method for nonlinear optimization problem solving. Modified Broyden method has been achieved applying the variable parameter, which values depend on the Broyden matrix convergence condition. In this paper the standard nonlinear optimization technique (Broyden methods for nonlinear equation solving) has been adopted according to Broyden matrix convergence condition and applied for visual servoing control problem which, due to the noise regularly present in the real systems, could be hardly control by the pure Broyden itself. The developed algorithm is verified by simulations for uncalibrated vision-guided robotic control and compared with four methods from literature, which have usually been used for the similar purpose. Modified Broyden method shows performance improvement over previous methods and is more robust in the presence of noise.