Multivariable sliding-mode extremum seeking control with application to alternator maximum power point tracking
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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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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)
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Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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