Iterative Learning on Dual-fuel Control of Homogeneous Charge Compression Ignition * *Financial support for this research provided by Biofuelnet Canada.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it