Chaos synchronization of brushes direct current motors for electric vehicle: Adaptive fuzzy immersion and invariance approach
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, a novel control technique on basis of adaptive immersion and invariance (I&I) theorem and fuzzy systems is proposed to control the brushless direct current motors (BLDCMs) for electric vehicles. The chaotic behaviour of the BDCMs is synchronized with the desired chaotic system. The parameters of the chaotic model are considered to be unknown and are online estimated with tuning rules that are derived through the I&I stability theorem. The effects of the adaptation errors are compensated by the adaptive fuzzy systems. The fuzzy systems are online learned to insure the robustness and stability. The effectiveness and desirable performance of the suggested methodology is verified with both normal simulations and real-time examination. The simulation outcomes of the presented methodology are compared with the some other adaptive methods and it is shown that the presented methodology is more effective.
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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