An Operator Control Theory Approach to the Design and Tuning of Constrained Model Predictive Controllers.
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Model Predictive Control algorithms minimize on-line and at every sampling point an appropriate objective function, subject to the satisfaction of possible hard constraints on the process outputs, inputs or other state variables. The presence of the hard constraints in the on-line optimization problem results in a nonlinear closed-loop system, even though the process dynamics are assumed linear. This paper describes a procedure for analyzing the nominal and robust stability properties of such control laws, by utilizing the Operator Control Theory framework.