New design of two‐dimensional <scp>LQ</scp> control for batch processes with iterative learning error compensation
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".