Self-tuning fuzzy logic PID controller, applications in nuclear power plants
Bibliographic record
Abstract
This paper presents intelligent control design using self-tuning fuzzy logic control technique. The proposed adaptive control design is used to maintain system parameters to be within design limits, which provided means to improve the efficiency of the overall control system. The proposed self-tuning control system is applied on breathing air control systems in nuclear power plant which was recommended to maintain system pressure within design limits. System dynamics were studied, transfer functions were built and controlling parameters were established to develop system model in Simulink. Relationship to system pressure, steady-state error, Kp, Ki and Kd was studied and used to build fuzzy controller. Controller was implemented and simulation results demonstrated that self-tuning fuzzy PID controller has better control, precision and performance in maintaining system pressure with reduced overshoot, rise time, settling time and steady state error compared against conventional PID controller. Fuzzy PID controller also proved to be huge cost saver for the company in long run.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".