A new design of double predictive proportional integral control strategy for first order plus dead time industrial processes
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".