Model‐guided extremum seeking–case studies
Bibliographic record
Abstract
Summary In practice, data‐driven control and optimization techniques are applied to address problems in engineering systems of which the model is either unavailable or so complicated that a model‐based analytic design can be hardly carried. Among them, the extremum seeking (ES) is a popular model‐free or data‐driven optimization method that has been effectively applied to provide optimal solutions to various industrial control systems. In this article, a new design philosophy, called the model‐guided ES, which is a special case of model‐guided data‐driven (MGDD) optimization, is presented and demonstrated with two successful case studies. In particular, it is shown that, in these two cases, how models of physical systems, even if imperfect or developed in a data‐driven way instead of the first‐principle based approach, could be integrated together with the conventional ES algorithm to deliver much improved and guaranteed convergence performance and the ultimate bound. It is noted that the first case is for the automotive diesel engine optimization and the second case for the automated regulation of LiDAR detection range. Both cases are successfully validated with experiments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".