Robust design of Terminal ILC with an Internal Model Control using μ-analysis and a genetic algorithm approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The thermoforming heater temperature setpoints can be automatically tuned with cycle-to-cycle control. Terminal Iterative Learning Control (TILC) is used to adjust the heater temperature setpoints so that the temperature profile at the surface of the plastic sheet converges to the desired temperature. Industrial thermoforming ovens generally have a large number of temperature sensors and heaters, which makes the design of TILC difficult. The proposed TILC design is based on Internal Model Control (IMC). The robustness of a closed-loop system with this TILC algorithm is measured using the μ-analysis approach. A Genetic Algorithm (GA) is used to find the IMC exponential filter parameters giving the most robust closed-loop system. Simulation results are included to show the effectiveness of this robust TILC algorithm.
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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 it