Machine Learning-based Diesel Engine-Out NOx Reduction Using a plug-in PD-type Iterative Learning Control
Bibliographic record
Abstract
A plug-in Iterative Learning Controller (ILC) is proposed to reduce the engine-out Oxides of Nitrogen (NOx) emissions of a medium-duty diesel engine. A control-oriented model is developed to simulate the dynamic behavior of NOx, Carbon Monoxide (CO), and unburned hydrocarbon (UHC) emissions as well as engine power output given by the break mean effective pressure (BMEP). This control-oriented model consists of a support vector machine (SVM) that calculates the steady-state values of the emissions and BMEP as a function of the engine speed, the amount of injected fuel and the injection rail pressure. The SVM-based model was then augmented using experimental results from a fast response electrochemical NOx sensor to predict the transient behavior of the engine. Finally, a plug-in PD-type ILC that consists of a PID and an ILC controller is developed to reduce the amount of engine-out NOx while controlling the desired engine power, represented by BMEP, and monitoring the other emissions. The proposed controller provides a powerful tool for engine-out emissions trade-off in addition to controlling the desired engine output power.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".