Iterative learning model predictive controller of plastic sheet temperature for a thermoforming process
Why this work is in the frame
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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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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 | 0.000 |
| 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