A new design of double predictive proportional integral control strategy for first order plus dead time industrial processes
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Bibliographic record
Abstract
Abstract When the Smith predictive controller controls the first‐order plus dead time process, it is too sensitive to the parameter changes of the system, leading to poor system stability and no practical application value. First, this paper derives and proves the equivalent form of the Smith‐proportional‐integral‐derivative (PID) control strategy as a predictive PI (PPI) control strategy. Second, this paper proposes a new double predictive PI control strategy (DPPI), where the DPPI controller mainly consists of a predictive PI controller and a predictor with an integral link. Again, for the integral predictor in the DPPI control strategy, two new regulation parameters are introduced, which can effectively regulate the control performance and robustness of the control system, improve the degree of freedom of the controller design, and give the principles for the adjustment of the DPPI controller parameters. Finally, it has been verified through simulation experiments that the proposed method can significantly improve response speed and effectively resist external perturbations with good control effect and robust stability.
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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)
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Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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