Multivariable sliding-mode extremum seeking control with application to alternator maximum power point tracking
Bibliographic record
Abstract
A common problem with conventional alternator-based energy converters in vehicular applications is that they do not work at optimal operating points in their speed-power curves. This paper addresses the above problem by utilizing a Switched- Mode Rectifier (SMR) load-matching technique using real-time extremum seeking control. To this end, a multivariable control strategy is presented to track the maximum power in an alternator-based system. In particular, we propose a novel multivariable sliding-mode technique for maximum power point tracking (MPPT) in a Lundell alternator system. The proposed controller, combines the merits of multivariable extremum seeking and sliding-mode control. Utilizing the multivariable extremum seeking control makes the control model-free and increases the controller efficiency. Besides, the sliding-mode controller is robust in the face of parametric and dynamic uncertainties. The proposed controller is compared with two recent multivariable and decentralized methods. The simulation results demonstrate the advantages of the proposed controller in terms of fast and precise convergence and robust performance in face of disturbances and uncertainties.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".