Robust Nonlinear Disturbance Observer Design for Estimation of Ammonia Storage Ratio in Selective Catalytic Reduction Systems
Bibliographic record
Abstract
Urea-based selective catalytic reduction (SCR) system is a promising way to obtain high NOx reduction and commonly adopted in diesel engine aftertreatment systems. The ammonia storage ratio is critical for SCR feedback control but it is difficult to be directly measured by sensors. This paper aims to effectively estimate the ammonia storage ratio on line and reduce the cost of using ammonia sensors. In the proposed method, the ammonia storage ratio is treated as an external disturbance in the NOx dynamic model and estimated by the nonlinear disturbance observer (NDO) methods. Furthermore, to reduce estimation errors of ammonia storage ratio caused by the high-frequency measurement noises, a novel robust nonlinear disturbance observer (robust NDO) is proposed and compared with a typical design method (regular NDO). Both the NDOs are developed based on part of the three-state SCR model and cost-effective, since NOx sensors are only used. The stability and noise attenuation properties of both estimations were also analyzed in the paper. The simulation results based on the full-vehicle simulator of FTP-75 test demonstrate that the regular NDO and the robust NDO can effectively estimate the ammonia storage ratio even in cases where ammonia cross-sensitivity affects the response. Among the two observers, the robust NDO has better noise attenuation properties.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".