Compound Control Strategy Based on Active Disturbance Rejection for Selected Catalytic Reduction systems
Bibliographic record
Abstract
Urea-based selected catalytic reduction (SCR) systems are effective ways in diesel engine after-treatment systems to meet increasingly stringent emission regulations. To achieve high NOx reduction efficiency and low NH3 slip, the control of the SCR system becomes more challenging, especially in transient operating conditions with model uncertainties. To effectively address this issue, this paper proposed a compound control strategy with a switching mechanism between an active disturbance rejection (ADR) controller and a zero-input controller. The ADR controller estimates and rejects the total (internal and external) disturbances from the SCR system when the exhaust gas temperature is high and its variation is small. The zero-input controller is used to lower ammonia surface coverage ratio to avoid high ammonia slip when exhaust gas temperature suddenly rises. The proposed control strategy is validated through a high-fidelity GT-Power simulation for a light-duty diesel engine over steady states and federal test procedure (FTP-75) test cycle. Its effectiveness is demonstrated especially in rapidly transient conditions with model uncertainties.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".