Robust feedback linearization-based congestion control using a fluid flow model
Bibliographic record
Abstract
In this paper, a new robust feedback linearization congestion control strategy for a fluid flow model is introduced. The recourse to a robust control technique permits us to use a non accurate dynamic model in order to design and analyze the controlled system. The fluid flow model (FFM) under its different types of variants is used for network performance evaluation and control as applied to congestion control. Validated by several researchers, the considered first order non linear model is simple in comparison to the detailed Markovian queuing probabilistic models, and it captures the dominant dynamic behavior of a wide range of queuing systems. The sliding mode generalized variable structure (SM-GVS) control recently introduced by M. Fliess which is based on differential algebra concepts allows the switching to take place on the highest derivative of the control input such that the main drawbacks of the discontinuous control that is the chattering is consequently reduced. In this paper, our proposed controller uses the feedback linearization-based SM-GVS approach with some convergence tuning parameters
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How this classification was reachedexpand
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)
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 teacher head, 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".