Regularized ${\rm RLS}$-$\lambda$ and DHOBE: An Adaptive Feedforward for a Solenoid Valve
Bibliographic record
Abstract
To allow a stable and fast acting hydraulic pressure control on a continuously variable transmission (CVT) for road vehicles, an adaptive feedforward strategy is used. The Dasgupta-Huang outer bounding ellipsoid (DHOBE) and recursive least squares (RLS) with exponential forgetting factor(RLS-λ) adaptation algorithms are compared to the non-adaptive feedforward. The experiments show a clear advantage for the adaptive over the non-adaptive version by compensating for the slow drift of the valve pressure gain during the warm-up period of the transmission. Because of highly correlated input data, the adaptation algorithms offer deceiving performances with oscillating identified parameters. A regularization procedure is added to both adaptation algorithms, giving the rRLS-λ and rDHOBE. The regularized algorithms offer significantly better performances and stability than their non-regularized counterparts. Because of its implicit parametric uncertainty calculation while keeping an equivalent convergence rate, and a lower number of updates, the rDHOBE algorithm is regarded as the best solution for the application. By adapting a simple linear model, the rDHOBE adaptive feedforward succeeds in responding to an abrupt change of the external pressure setpoint with no added actuation delay while keeping the pressure error under 0.5 bar.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".