Automotive coldstart emissions reduction using MIMO sliding mode control with actuator saturation
Bibliographic record
Abstract
In this study, a modified version of multi-input multi-output (MIMO) sliding mode control technique is applied to reduce the total hydrocarbon emissions of an automotive engine during the coldstart period. This specific control design technique is suitable for MIMO systems with constraints on their input signals and it allows finding a set of upper limits for the gains of sliding mode controller (SMC) with regard to the given input constraints. In this context, an optimization problem is formulated to calculate the upper bounds of SMC gains to ensure the feasibility of the calculated control commands for the considered coldstart control problem. Through simulations, it is indicated that the devised SMC can properly track the given exhaust gas temperature (T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">exh</sub> ) and engine-out hydrocarbon emission (HC <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">raw-c</sub> ) desired profiles in the presence of external disturbances without violating the existing engine system input constraints.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".