Input-output Data-driven Modeling and MIMO Predictive Control of an RCCI Engine Combustion
Bibliographic record
Abstract
This study presents a data-driven identification method based on Kernelized Canonical Correlation Analysis (KCCA) approach to generate a state-space Linear Parameter-Varying (LPV) dynamic representation for the RCCI engine combustion. An LPV model is used to estimate RCCI combustion phasing (CA50) and indicated mean effective pressure (IMEP) based on fuel injection timing and quantity. The proposed data-driven method does not require prior knowledge of the plant model states and adjusts number of states to increase the accuracy of the identified state-space model. The results demonstrate that the proposed data-driven KCCA-LPV approach provides a dependable technique to establish a fast and reasonably accurate RCCI combustion model. The established model is then incorporated in a design of a constrained MIMO Model Predictive Controller (MPC) to track desired crank angle for 50% fuel burnt and IMEP at various engine conditions. The controller performance results demonstrate that the established data-driven constrained MPC combustion controller can follow desired CA50 and IMEP with less than 1.5 CAD and 37 kPa error, respectively.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".