High‐order data‐driven optimal TILC approach for fed‐batch processes
Bibliographic record
Abstract
A high‐order data‐driven optimal terminal iterative learning control (H‐DDOTILC) is proposed for fed‐batch processes, which are considered a general class of nonlinear and non‐affine systems. A new dynamical linearization is introduced to the iteration domain to reveal the relationship of system terminal output and control input among batches. The proposed H‐DDOTILC consists of a high‐order learning control law, an iterative parameter estimator, and a rest algorithm, together. The learning control law with a high‐order form is capable of utilizing more control knowledge of the previous l batches to improve control performance. The parameter updating law is used to estimate the unknown derivatives of the nonlinear system to control input, which is the main part of the nonlinear learning gain function of the control law. Essentially, the proposed approach is a data‐driven control strategy, and the controller design and analysis only depend on the I/O data of the plant, which is a distinct feature for the control problems of practical nonlinear and non‐affine systems. Both the rigorous analysis and the simulation results illustrate the applicability and effectiveness of the proposed approach.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".