Preservation of dissipativity under multirate sampling with application to nonlinear H<inf>&#x221E;</inf> control
Bibliographic record
Abstract
This paper deals with a common practical problem where the output of a nonlinear sampled-data system is constrained to be measured at a relatively lower sampling rate. Designing a continuous-time controller that satisfies a specific dissipation inequality, the dissipativity of the digitally implementation of the emulated controller in a multirate control scheme is analyzed. It is shown that the closed-loop multirate system preserves similar dissipation inequality for the state feedback law in a semiglobal practical sense. Moreover, we propose a unified framework for designing nonlinear multirate sampled-data control systems in presence of disturbance inputs via emulation method and the multirate nonlinear H∞control is addressed as a special application. Simulation results validate that the H∞performance criterion is achieved not only with a preferable behavior but also under much lower measurement sampling rate, in comparison with the fast single-rate setup.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".