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This paper introduces the implementation of a recently introduced method suitable for visual servoing. The method is based on the generalization of secant methods for nonlinear optimization. The difference with existing approaches related to visual servoing is that we do not impose a linear model to interpolate the goal function. Instead, we prefer to identify the linear model by building the secant model using population of the previous iterates, which is as close as possible to the nonlinear function, in the least-squares sense. The new system has been shown to be less sensitive to noise and exhibits a faster convergence than do conventional quasi-Newton methods. The theoretical results are verified experimentally and also by simulations.