Discrete‐time ${\cal H}_{\rm 2}$ output tracking control of wireless networked control systems with Markov communication models
Bibliographic record
Abstract
Abstract This paper considers the discrete‐time ${\cal H}_2$ output tracking control of wireless networked control systems (NCSs) where the time delays are modeled as Markov chains. Output tracking control can find many applications in industry. In order to reduce the conservativeness and achieve better performance, the designed state feedback controller is dependent on available sensor‐to‐controller and controller‐to‐actuator delays. Then, the formulated closed‐loop system is a special jump linear system governed by interdependent parameters of Markov chains and the condition for stochastic stability is proposed. By generalization of the ${\cal H}_2$ norm definition, new relation of the ${\cal H}_2$ norm for the special system is derived in terms of state space form. The condition of a set of linear matrix inequalities (LMIs) with nonconvex constraints is given to solve the ${\cal H}_2$ output tracking control problem. Simulation examples are provided to illustrate the effectiveness of the method. Copyright © 2009 John Wiley & Sons, Ltd.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".