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In industrial control systems, practical interest is driven by the fact that today?s processes need to be operated under tighterperformance specifications. Often these demands can only be met when process nonlinearities are explicitly considered inthe controller. Nonlinear predictive control, the extension of well-established linear predictive control to nonlinear systems,appears to be a well-suited approach for this kind of problems. In this paper, an optimal nonlinear predictive controlstructure, which provides asymptotic tracking of smooth reference trajectories, is presented. ; The controller is based on afinite?horizon continuous time minimization of nonlinear predicted tracking errors. A key feature of the control law is thatits implementation does not need to perform on-line optimization, and asymptotic tracking of smooth reference signal isguaranteed. An integral action is used to increase the robustness of the closed-loop system with respect to uncertainties andparameters variations. The proposed control scheme is first applied to planning motions problem of a mobile robot and,afterwards, to the trajectory tracking problem of a rigid link manipulator. Simulation results are performed to validate thetracking performance of the proposed controller.