Iterative Learning on Dual-fuel Control of Homogeneous Charge Compression Ignition * *Financial support for this research provided by Biofuelnet Canada.
Bibliographic record
Abstract
An Iterative Learning Controller (ILC) is used to control a dual-fuel Homogeneous Charge Compression (HCCI) engine. The engine is a CFR engine with in-cylinder pressure measurement ports and is operated at 100°C intake heating, 800 RPM and a compression ratio of 11:1. To control combustion timing and load, the amount of iso-octane and n-heptane injected into the manifold are used as inputs. The metrics used for combustion timing and load are CA50, crank angle when 50% of the fuel is burned, and gross IMEP, respectively. Using these inputs and outputs a system identification is performed and an ARMAX model is obtained. This model is then used to generate a norm optimal control. The norm optimal control is compared to a model-less control strategy that involves populating the off-diagonal of the learning matrix using a Jacobian estimate inverse. Both systems are used to follow a reference trajectory involving a step input in IMEP then CA50. The model-less control outperforms the norm optimal in both convergence speed and final iteration error. Application of non-causal filters within the iteration is also tested using a zero-phase filter and a Gaussian filter. The zero-phase has faster convergence than either the Gaussian or filter-less and has better final iteration error. This gives the best ILC control as model-less with zero-phase filter. This control is then compared with two PI controllers. It is found that the ILC outperforms the PI controllers after 3 iterations.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".