On‐line Tuning of Model Predictive Controllers Using Fuzzy Logic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This paper addresses the problem of tuning model predictive controllers for good performance. An automatic online tuning strategy is developed to adjust the prediction horizon, P , the diagonal elements of the input weight matrix, Λ, and the diagonal elements of the output weight matrix, Γ. The control horizon is left constant because its relative value with respect to P is more important. The tuning algorithm is based on the fuzzy logic concepts. Predefined fuzzy rules that formulate the general tuning guidelines available in the literature and the performance violation measure in the form of fuzzy sets determine the new tuning parameter values. Therefore, the tuning algorithm is cast as a simple and straightforward mechanism with modest computational requirements. This feature makes it more appealing for online implementation. The effectiveness of the proposed tuning method is tested through simulated implementation on a binary distillation column example and on a non linear CSTR example. The result of the simulations revealed the success of such a method.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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)
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 it