New design of two‐dimensional <scp>LQ</scp> control for batch processes with iterative learning error compensation
Why this work is in the frame
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Bibliographic record
Abstract
Abstract This paper proposes a two‐dimensional infinite horizon linear quadratic iterative learning control (2D‐IHLQILC) strategy based on error compensation. The strategy aims to address the shortcomings of one‐dimensional infinite horizon linear quadratic control (1D‐IHLQC), which is unable to utilize historical batch information. Firstly, a novel extended state space model is established to describe the batch process, which provides more degrees of freedom for the controller design and enables additional adjustment of the batch process input increment and output increment. Secondly, an error compensation strategy is introduced using historical batch information, which extends the novel extended state‐space model from one to two dimensions and can effectively deal with the time delay problem. Finally, a novel 2D‐IHLQILC controller is designed, which empowers the controller to learn iteratively from historical batch information to improve the control performance batch by batch and to achieve full tracking of the setpoint trajectory. The effectiveness of the 2D‐IHLQILC is tested on the holding pressure control in the injection moulding process as an example.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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 it