Adaptive Sliding Mode Control for Maximum Power Point Tracking in Photovoltaic Systems
Bibliographic record
Abstract
Nowadays, photovoltaic (PV) systems are widely used in daily life and in many critical fields such as agriculture, industry, and exploration.Designing a controller to ensure that the PV system consistently achieves high efficiency during operation is always of interest to the scientific community.The PV system must maintain operation at the maximum power point tracking (MPPT) to optimize efficiency.Beyond the influence of intrinsic parameters like temperature and radiation, it is also significantly affected by external disturbances and variations in the power conversion circuit.Hence, an effective control strategy is required to mitigate these impacts.This paper introduces an adaptive sliding mode control (ASMC) approach for MPPT in PV systems.Initially, the Perturb & Observe (P&O) algorithm determines the reference voltage for the control scheme.Then, an adaptive sliding mode controller is designed to accurately track this reference while an observer estimates uncertainties and external disturbances.To further minimize chattering effects, a fuzzy controller is incorporated.The stability of the proposed controller is guaranteed based on the Lyapunov criterion, ensuring both adaptability and robustness.Finally, comparative simulations are conducted to validate its performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".