This paper examines the use of an interior point strategy to solve multilevel optimization problems that arise
from the inclusion of the closed-loop response of constrained, linear model predictive control (MPC) within
a primary quadratic or linear programming problem. We motivate the formulation through its application to
optimizing control problems, although the strategy is applicable to several problem types. The problem is
cast as a dynamic optimization problem in which an optimal steady-state operating point is sought, subject
to constraints on the closed-loop response of the system under constrained predictive control. Because a
quadratic programming (QP) problem must be solved at every sampling period, the resulting problem is
multilevel in nature. The formulation approach used in this paper is to include the Karush−Kuhn−Tucker
(KKT) conditions that correspond to the MPC quadratic programming subproblems as constraints within a
single-level optimization problem. The resulting complementarity constrained optimization problem is shown
to be reliably and efficiently solved using an interior point approach. The method is applied to two case
studies, and its performance is compared to an alternative mixed-integer programming solution approach.
The design of a plant can significantly affect its inherent ability to be satisfactorily controlled.
Approaches for incorporating dynamic operability requirements within an optimal design
framework have been proposed, where a dynamic model of the plant and its associated control
system are included as constraints. This paper focuses on the inclusion of actuator saturation
effects in formulations of this type. A mathematical description of actuator saturation that is
suitable for incorporation within a simultaneous optimization framework is used. The optimization formulation is presented and its application demonstrated through two case studies.
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