Data‐driven optimal terminal iterative learning control with initial value dynamic compensation
Bibliographic record
Abstract
Iterative learning control is an effective control strategy for control of batch processes and initial condition is one of the most important factors affecting convergence of iterative learning batch process control. In this study, a novel initial value dynamic compensation‐based data‐driven optimal terminal iterative learning control (IDC‐DDOTILC) approach is proposed for non‐linear systems under random initial conditions. The unknown influence on the terminal output caused by the initial states is deduced by using a dynamical linearisation of the controlled non‐linear system along the iteration direction, and then the unknown influence is estimated iteratively and incorporated into the learning control law. As a result, the proposed IDC‐DDOTILC can drive the terminal output of the plant to attain the target value at the endpoint asymptotically under iteration‐varying initial conditions. Two chemical engineering examples including a batch reactor and a fed‐batch ethanol fermentation process are used to demonstrate effectiveness of the proposed control algorithm.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".