Optimal Sampling Rate and Quantization for Networked Control Systems
Bibliographic record
Abstract
Sampling and quantization in Networked Control Systems (NCS) are addressed in this paper. The NCS studied in this paper consists of a continuous time plant, a sensor network and a discrete time controller. The amount of network induces delay to the control system is a function of the sampling rate of the control system. An upper bound for the delay will be found using network calculus which is a theory for deterministic queuing. From the control side of view, we consider a delayed sampled data system and propose a method to study the effects of sampling and delay in a unified framework. We define the quality-of-control in the sense of a ${\mathcal{H}_\infty }$ norm of the system and we propose a method to minimize this norm. According to our result, the optimal solution may not corresponds to the lowest sampling rate and it depends on the dynamics of the system and parameters of the communication link. We also extend the result to investigate the effect of quantization by proposing a new finite level quantizer. Similar to the sampling rate, we show that a larger number of allocated bits for quantization does not necessarily result in a better quality-of-control.
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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 | 0.002 | 0.008 |
| 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 | 0.002 |
| 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".