A Robust LQR-Based Fuzzy-Immune PID Applied for a Greenhouse Temperature Networked Control
Bibliographic record
Abstract
Bioinspired control system strategies have received considerable attention in recent years, demonstrating their effectiveness to control various classes of systems. This paper presents a new robust LQR-based fuzzy-immune PID controller synthesis method, the particularity of this structure is the combination of the features granted by the linear quadratic regulator LQR and the fuzzy-immune PID controller as: robustness, optimization and self-adapting. The proposed approach is applied to the temperature control of a greenhouse system over Ethernet network. Such control loop is bound to be affected by several uncertainties such as communication delays, packet dropout... etc., it is therefore necessary to adopt a robust control strategy that can handle these problems. MATLAB/Simulink is used to simulate the proposed controller structure under several perturbations/uncertainties scenarios. The results show better time domain performance and greater robustness against uncertainties, in comparison with other PID structures (classic and fuzzy immune PID).
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".