LPV Controller Design for Diesel Engine SCR Aftertreatment Systems based on Quasi-LPV Models
Bibliographic record
Abstract
This paper presents linear parameter-varying (LPV) controller design for the urea-based selective catalytic reduction (SCR) system in diesel engines to reduce nitrogen oxides (NOX) and ammonia (NH3) emissions. Although such LPV SCR controller design has been previously developed, this paper extends it in various ways. The extension includes the usage of NH3 slip sensor for feedback LPV control, the adoption of NOX and NH3 measurements downstream of the catalyst as gain-scheduling parameters, the simultaneous design of feedforward and feedback LPV controllers, and a robustness analysis of the LPV controllers. Quasi-LPV SCR models derived from an existing control-oriented nonlinear parameter-varying model are utilized in the LPV controller design. The LPV controller performance is demonstrated based on an SCR simulation utilizing experimentally obtained engine-out NOX, and exhaust gas temperatures and flow rates. It is shown that the LPV controller provides satisfactory emission performance, as well as robustness against sensor noise and model parameter uncertainty.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".