H<sub>∞</sub> control of networked control systems with stochastic measurement losses
Bibliographic record
Abstract
In this paper, the modeling and H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> , control problems for the Networked Control System (NCS) with random data-packet losses in sensor-controller channel and disturbances are investigated. In specific, the networked system with sensor measurement losses is modeled as a Markovian jump linear system(MJLS) by using a multi-rate sampling approach, while the characteristics of network-induced packet losses are assumed to follow multiple-state Markov chain process. The sufficient conditions for the robustly stochastic stability of the NCS are obtained via the piecewise Lyapunov function method. Instead of designing a general robust controller, we take the dynamic network conditions into consideration when designing a statefeedback controller for the NCS. The stochastic packet-loss-dependant controller for the closed-loop networked system is presented in the formulation of linear matrix inequalities(LMIs), under the given H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> disturbance-rejection-attenuation level. Simulation results of a simple networked robotic arm show that the developed packet-loss-dependant controller can stabilize the networked system with both random sensor-measurement losses and disturbances robustly.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".