Robust Control with Genetic Algorithms of a Permanent Magnet Synchronous Machine Driving an Elastic Load Variable Inertia
Bibliographic record
Abstract
<p class="zhengwen"><span lang="EN-GB">The goal of this work is to contribute to the conception rules of the synthesis law control in order to give them a robust character. We study the issue of controlling of the synchronous motor driving a mechanical load. This load, driven by an elastic joint, presents a variable and bounded inertia. The synthesis of simple correctives and methods of the best corrective in the parameter variation interval are presented. In this paper, the synthesis rules of a PID regulator on which we added an optimization iterative method based on the system behavior expertise and genetic algorithm. This phase made it possible to give simple rules of synthesis and the parameterization of optimization method so as to find the three optimal degrees of correction freedom. The results are validated by simulation by means of MATLAB Simulink and present a better dynamic performance of the proposed control law.</span></p>
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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 | 0.000 | 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 | 0.001 | 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".