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An efficient approach for nonlinear model predictive control is proposed. Basically, the model is first linearized by feedback, secondly a model predictive control scheme, implemented with an optimized dynamic model and running within a small sampling period, is exhibited. Major simulation results performed using numerical values of an industrial SCARA type robot prove the effectiveness of the proposed approach. The nonlinear model-based predictive control and the commonly used computed torque control are compared. The tracking performances and the robustness with respect to external disturbances or model/robot mismatch are described.