Iterative learning model predictive controller of plastic sheet temperature for a thermoforming process
Bibliographic record
Abstract
Properties of the thermoforming process, such as its nonlinear, time-varying dynamics and actuator constraints, make its control challenging. An iterative control technique along with model predictive control (MPC) is presented in this paper on 2D control of the thermoforming process. This approach utilizes not only incoming information from the ongoing cycle, but also the information stored from the past cycles. To deal with constraints as well as non-repetitive disturbances in the process, the MPC technique is incorporated to update the control law within the cycle. To exploit the repetitive nature of the heating phase of the process, a cycle-to-cycle iterative learning control technique direction is proposed. The iterative learning strategy is useful for achieving desired temperature despite model mismatch and disturbances. Even though the proposed multi-zone temperature controller can handle a multivariable process, the large number of computations makes it difficult to apply to large systems such as a thermoforming machine. To reduce the computational burden, the control laws are computed offline using multi-parametric programming.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".