Design of a Robust Nonlinear Model Predictive Controller Based on a Hybrid Model and Comparison to Other Approaches
Bibliographic record
Abstract
A methodology to systematically design a model-based nonlinear model predictive controller is presented. The controller is referred to as hybrid since it uses the first-principles model to calculate the value of the controlled variables along the prediction and control horizons whereas uses the empirical model to ensure a terminal condition that accounts for model errors. The empirical Volterra series model was split into nominal and uncertain parts that were then used to formulate a structured singular value based robustness test. The proposed hybrid controller was compared against a robust empirical that uses solely an empirical model and to a nonrobust first principles model based nonlinear model predictive controller. To show the benefits of considering robustness in the controller formulation, extensive simulation studies were conducted that considered mismatch between the real process parameters and the model parameters. It is shown that in some case the performance of the hybrid controller can be superior to the purely empirical and to the first principles based controllers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".