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A new control scheme for the uncalibrated robotic visual tracking problem is proposed that compromises the computational expenses of overall system with offline modeling and online control. A nonlinear visual mapping model for the uncalibrated hand-eye coordination is first proposed with an artificial neural network implementation. An online visual tracking controller is then developed together with a real-time motion planner. To improve the system performance, the control scheme is also integrated with a feedforward controller to compensate unknown object motions. Extensive simulations and experiments demonstrate the effectiveness of the proposed control scheme.