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Trajectory of an underactuated manipulator is usually generated according to both kinematics and dynamics of the manipulator, different to that of conventional manipulator. The real trajectory by a real system may differ greatly from the trajectory generated from the dynamics model because there always exist errors in the dynamic model, and the feedback control is less effective in an underactuated manipulator. A method for generation of optimal trajectory for the real system of an underactuated manipulator with nonholonomic constraints is proposed. By using this method, the dynamics model of a real system can be improved by learning, and an optimal trajectory is generated according to the model improved sequentially. The effectiveness of the method is confirmed by experiment with a golf swinging robot. The implementation and experimental results obtained of the control method are described.