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Nonlinear model predictive control (NMPC) strategies based on linearization about predicted system trajectories enable the online NMPC optimization to be performed by a sequence of convex optimization problems. The approach relies on bounds on linearization errors in order to ensure constraint satisfaction and convergence of the performance index during the optimization at each sampling instant and along closed loop system trajectories. This paper proposes bounds based on robust tubesdoi:10.1109/tac.2011.2135190 fatcat:y73p6xxhurhgrnqs4bk7np2cn4