A Novel Robust Control for Disturbed Uncertain Microbial Fuel Cell with Noisy Output
Bibliographic record
Abstract
M icrobial fuel cell (M FC) is one of the most important renewable sources for energy supply and reduction of environmental pollution, which has affected by various adversities due to operating conditions.In this paper, there are several serious issues related to the stable operation of a microbial fuel cell that have considered in the design of the controller, including: 1-Nonlinear terms that are of hard type; 2-Uncertainty of the model which is of parametric type and includes changes in temperature, environment and concentration; 3-Disturbances into the system which are of both matched and unmatched types; 4-And noise on the fuel cell output which has different origins.Also, the nonlinear model of M FC has considered for a more accurate description of system dynamics.By using of output feedback, adaptive, and sliding mode methods, and developing an approximation based on chebyshev neural network, a novel robust hybrid technique has proposed for controlling M FC output voltage and power.Using chebyshev neural network which has a simple structure with a suitable computational volume, the uncertainties, disturbances and hard nonlinear terms have approximated, and the optimal weights of the approximation have obtained by designing adaptive laws.Also, the robust part of the controller eliminates the effects of estimation error and noise.The Lyapunov' s theory has used to ensure the stability of the closed-loop system.Furthermore, simulation in M ATLAB environment and making comparison with the recent three robust methods in a strong scenario shows the efficiency of the proposed control method.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".