Air-Fuel Ratio Control of Spark Ignition Engines Using a Switching LPV Controller
Bibliographic record
Abstract
The three way catalytic converter (TWC) is a critical component for the mitigation of tailpipe emissions of modern spark ignition internal combustion (IC) engines. Because the TWC operates effectively only when the air-fuel ratio is very close to stoichiometric, accurate control of the air-fuel ratio is required. This paper uses a switching linear parameter varying (LPV) controller to regulate the air-fuel ratio. For controller design purposes, the dynamics of the fuel path is modeled as a time-varying first-order plus dead time (FOPDT) model, varying with the engine operating point, i.e., engine speed and air flow. Large variation of the FOPDT model across the engine operating range leads to a conservative LPV controller. Therefore, the operating range is divided into smaller subregions, an individual LPV controller is designed for each, and the LPV controllers are then switched based on the operating point. The LPV controllers are found by solving a convex optimization problem with linear matrix inequalities (LMIs) which can be efficiently solved using available LMI techniques. The resulting closed-loop system has guaranteed performance over the operating range of the engine. Simulations show the improved air-fuel ratio regulation of the switching LPV controller over the engine's operating range compared to that of an H∞controller which is scheduled based on air flow only as well as a non-switching LPV controller.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".