A new design of predictive plus <scp>PID</scp> control for second order plus time delay systems
Bibliographic record
Abstract
Abstract This paper proposes two novel predictive proportional‐integral‐derivative (PID) controllers for second‐order non‐self‐balance systems and self‐balance systems with time delays. For second‐order non‐self‐balance systems with time delays, traditional predictive PID controllers suffer from the drawback of failing to return to the setpoint after disturbances. Therefore, this paper introduces a novel double predictive PID controller, termed PPI‐PPD controller, which consists of an inner‐loop predictive PD controller and an outer‐loop predictive PI controller. In second‐order self‐balance systems encountered in practical chemical processes, time delays can lead to longer adjustment times, increased overshoot, and divergence issues. In order to solve these problems, an improved predictive PID (PPID) controller is proposed in this paper, which introduces a compensation link and an anti‐interference link in the traditional predictive PID controller. This enhancement improves the dynamic response and disturbance resistance of the control system. Simulation results demonstrate that both novel predictive PID controllers exhibit excellent control performance and robustness.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".