Simultaneous Design and Control: A New Approach and Comparisons with Existing Methodologies
Bibliographic record
Abstract
In this work, a new methodology to conduct simultaneous design and control of large-scale systems is presented. The proposed method uses a structured singular value norm calculation (μ) to estimate the worst-case disturbance profile. This profile is then used to simulate the closed-loop nonlinear dynamic process model for obtaining the worst-case output variability and to test the process feasibility constraints. Thus, the proposed method referred to as the hybrid worst-case approach (HWA) methodology combines the analytical μ calculation of the worst-case disturbance and dynamic simulations using the mechanistic closed-loop process model to calculate variability. To test the proposed HWA method the integration of design and control of the reactor section of the Tennessee Eastman process was analyzed. This case study was also used to compare the proposed HWA method to other previously reported methodologies. Although the results obtained by the present HWA method for the case study are slightly conservative, the computational demand required by the present method is found to be 1 order of magnitude smaller than that required by a dynamic optimization-based methodology.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".