Integration of Design and NMPC-Based Control under Uncertainty and Structural Decisions: An MPCC-Based Approach

Abstract

In this work, we investigate the challenges and limitations of the application of nonlinear model predictive control (NMPC) for the integration of design and control for systems subject to structural decisions and model uncertainty. The problem involving discrete and continuous decisions is referred to as a mixed-integer bilevel programming model (MIBLP), which cannot be directly solved with conventional MINLP solvers. To address this issue, we implement a classical KKT transformation strategy to transform the original MIBLP into a single-level MINLP. The KKT conditions for the NMPC are determined and incorporated as constraints in the problem for process design. A regularization strategy is implemented to reformulate the complementarity constraints. Then, the single-level MINLP is directly solved with a branch and bound strategy. The proposed approach is tested in a reaction system network subject to uncertainty. The performance of a nominal- and a robust-NMPC control approaches are compared in the presence of process disturbances. Results indicate that the strategy with a robust-NMPC returns a more conservative process design with better control performance compared to results with a nominal-NMPC.