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A strategy based on Nonlinear Programming (NLP) sensitivity is developed to establish stability bounds on the plant/model mismatch for a class of optimization-based Model Predictive Control (MPC) algorithms. By extending well-known nominal stability properties for these controllers, we derive a sucient condition for robust stability of these controllers. This condition can also be used to assess the extent of model mismatch that can be tolerated to guarantee robust stability. In this derivation we deal with MPC controllers with ®nal time constraints or in®nite time horizons. Also for this initial study we concentrate only on discrete time systems and unconstrained state feedback control laws with all of the states measured. To illustrate this approach we give two examples: a linear ®rst-order dynamic system and a nonlinear SISO system involving a ®rst order reaction. #

Recently, the unscented Kalman filter (UKF) algorithm, which is a new generalization of the Kalman filter for nonlinear systems, was proposed in the literature. It has significant advantages over its widely used predecessor, the extended Kalman filter (EKF). These include better accuracy and simpler implementation and the dispensability of system and measurement model differentiability. In this work, we compare the performance of the two approaches in a simulated pH process with three situations considered. The first one evaluates the performance differences between the unscented transform and the EKF linearization, as applied to the nonlinear pH output equation. In the second simulation, the complete filter algorithms are compared in a state estimation framework. The third case concerns parameter estimation. In all three cases, the UKF produced more-accurate results.

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