Dissipative Tracking Control of Nonlinear Markov Jump Systems With Incomplete Transition Probabilities: A Multiple-Event-Triggered Approach
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Bibliographic record
Abstract
This article deals with the problem of multiple-event-triggered dissipative tracking control for nonlinear Markov jump systems with incomplete transition probabilities. An interval type-2 fuzzy model with partially known transition probability matrix is used to capture the underlying nonlinearities and a hidden Markov model with an incomplete conditional probability matrix is employed to describe the possible asynchronous phenomenon between the plant and the tracking controller. A multiple-event-triggered methodology involving two adaptive event-triggered schemes for the actuator channel and the sensor channel is proposed. By using the Lyapunov and dissipativity theory, sufficient conditions for the desired tracking controller are established in terms of linear matrix inequalities. Last, two examples, involving one numerical and one practical model named the Hénon system, are utilized to show the effectiveness of the proposed tracking control algorithm.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it