An Extremum Seeking Strategy Based on Block-Oriented Models: Application to Biomass Productivity Maximization in Microalgae Cultures
Bibliographic record
Abstract
This paper proposes an adaptive slope seeking strategy targeting any reachable operating point—including extremum—on the input/output map of a general dynamic single input single output (SISO) system approximated either by a quadratic Hammerstein, Wiener, or Wiener–Hammerstein model. The proposed control strategy is based on a recursive estimation algorithm which is used to estimate the model parameters, a slope reference generator, and a controller. A new algorithm called auxiliary model-recursive prediction error method (AM-RPEM) is used for the first task, and it is shown that the three model structures are equivalent from an identification point of view. Using the estimated parameters, a slope reference generator is proposed together with a self-governed pole placement controller with integral action, which advantageously replaces the heuristic integrator gain tuning in classical extremum-seeking schemes. Finally, the proposed control strategy is tested in simulation, first with a numerical example and then, using a dynamic model of Isochrysis galbana cultures so as to achieve concurrently extremum-seeking and suboptimal control. Simulation results using a recursive least squares algorithm and the proposed AM-RPEM are discussed.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 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 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".