Multivariable sliding-mode extremum seeking control in power electronic systems
Bibliographic record
Abstract
This thesis investigates the design and implementation of extremum seeking control with application to power electronics.To this end, a novel multivariable sliding-mode extremum seeking (MSES) scheme is developed and applied to several control and optimization problems involving maximum power point tracking (MPPT) and motor drives.The behavior of the controller in terms of convergence characteristics and stability is studied using nonlinear systems analysis tools.The proposed MSES is utilized in three applications.First, we apply the concept to MPPT in an alternator-based energy conversion system.The objective is to achieve optimal power conversion at different speeds and output voltages of a Lundell alternator.The performance of the proposed controller is experimentally verified on a laboratory-scale setup through controlling the alternator field current and output voltage to gain fast and precise convergence and robust performance in face of disturbances and uncertainties.In the second application, the proposed MSES is used to tune a proportional-integral (PI) controller which regulates the current of a permanent magnet synchronous motor (PMSM).The performance of the proposed MSES tuning method in terms of accuracy, parametric variations, and load torque disturbances is investigated through several experimental tests on a PMSM setup.In the third application, the MSES concept is extended to a PMSM-drive system which emulates an exercise machine working at low speeds.In this case, the algorithm is modified to a multi-objective sliding-mode extremum seeking (MOES) optimization scheme for torque control of a PMSM as well as minimization of its torque ripples.To this end, the MSESC method is utilized to implement an adaptive iterative learning control (AILC) strategy for torque ripple minimization.The performance of the proposed MOES in terms of torque ripple suppression, steady state and transient performance, and load disturbance rejection is experimentally verified through synthesizing different mechanical impedances.
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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 | 0.001 |
| 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 | 0.001 | 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".