H<sub>∞</sub> control of networked control systems with stochastic measurement losses
Bibliographic record
Abstract
In this paper, the modeling and H∞, 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∞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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".