Feedback/feedforward modeling and control of electroosmotic flow in a T-shape microchannel
Bibliographic record
Abstract
Electroosmotic effect is usually utilized to generate the flow field in microfluidic systems. In many of these microdevices, an accurate control over the output flow rate of the microfluidic part is necessary for the successful operation of the whole system. In this study, a combined feedback/feedforward strategy is proposed to control the output flow rate in a micro-T-junction. First, finite element model of the electroosmotic flow in the T-junction is generated; second, using the adaptive neural fuzzy inference system, the finite element model forms a basis for generating training data for building an inverse model of the flow in the micro-T-junction. This inverse model serves as a controller in the feedforward part of the system. Then, in order to make the controller robust against disturbances and uncertainties such as dimensional tolerances, a Mamdani-type fuzzy logic controller is incorporated in the feedback part of the controller. Finally, simulation results are presented in order to proof the performance of the designed controller.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".