IMC based iterative learning control of DOC temperature during DPF regerneration
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Bibliographic record
Abstract
Control of Diesel Oxidation Catalyst (DOC) outlet temperature is critical for the downstream Diesel Particulate Filter (DPF) regeneration. However, the complexities of the reactions in DOC make it difficult to manage its outlet temperature due to model uncertainties including time delay mismatch. DPF regeneration is treated as a batch process and the Internal Model Control (IMC) based Iterative Learning Control (ILC) was used for DOC outlet temperature control in this paper. The IMC-based ILC consists of the standard IMC and historical data based ILC. The standard IMC is based on the process model with time delay identified from the high fidelity DOC model in GT-Power. The ILC is designed based on historical information including the controller input, plant output, and model predictive output stored in the `memory'. Simulation results through high-fidelity GT-Power model are compared with IMC alone method and show that IMC based ILC can have fast and non-overshoot tracking after several iterations.
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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