Limits of control performance for distributed networked control systems in presence of communication delays
Why this work is in the frame
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Bibliographic record
Abstract
Summary In terms of computational complexity and fault tolerance, distributed networked control systems (DNCSs) is favorable for large‐scale processes. However, it poses additional limitations on the achievable control performance, especially when communication delay is present. The conventional minimum variance (MV) benchmarks mainly consider the limitations caused by the system itself and can give overly estimates of achievable performance when applied to the systems under distributed networked control. This paper proposes a solution to the MV benchmark for DNCSs considering both system time delays and time‐invariant communication delays. Furthermore, lower and upper bounds of the MV benchmark are proposed to assess the performance of DNCSs when there are time‐varying communication delays. These results are useful for evaluating the potential performance improvement if a DNCS is implemented to replace a decentralized control system. The proposed results are illustrated by a simulation example.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it