Integration of Design and NMPC-Based Control under Uncertainty and Structural Decisions: An MPCC-Based Approach
Bibliographic record
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".