Super-Twisting Sliding Mode Control for Markovian Jump Systems Based on Quantized Output-Feedback
Bibliographic record
Abstract
This paper focused on designing the super-twisting algorithm (STA)-based output- feedback sliding mode control (SMC) for multi-input Markovian jump systems under digital channel transmission. Specifically, the uniform quantization strategy is employed to implement the network communication for both the measured outputs and the sliding variables. It is shown that the novel quantized-data-based output- feedback STA can guarantee the practical reachability of the sliding variable with probability one by means of a dynamic adjustment policy for quantizers' parameters. Sufficient conditions for the existence of the feasible STA parameters and output- feedback SMC gains are proposed in terms of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nonconvex</i> equalities and inequalities, which can be solved effectively via a modified genetic algorithm combining the gridding search technique. Finally, a numerical example is provided to verify the effectiveness of the proposed STA-based SMC scheme for Markovian jump system via quantized output- feedback.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".