Robust H∞ control for uncertain stochastic systems with state delay
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Full frame distilled prediction
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.
- Candidate categories
- Meta-epidemiology (narrow)
- Consensus categories
- none
- Domain
- Candidate signal: noneConsensus signal: none
- Study design
- Candidate signal: Simulation or modelingConsensus signal: Simulation or modeling
- Genre
- Candidate signal: EmpiricalConsensus signal: none
- Teacher disagreement score
- 0.990
- Threshold uncertainty score
- 1.000
- Validation status
machine_predicted_unvalidated·codex-gemma-dda1882f352a
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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.
- Teacher spread
- 0.183 · how far apart the two teachers sit on this one work
- Validation status
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
Abstract
This note deals with the problems of robust stochastic stabilization and robust H ∞ control for uncertain stochastic systems with a time-varying delay in the state. The parameter uncertainties are assumed to be time-varying norm-bounded appearing in both the state and input matrices. The purpose of the robust stochastic stabilization problem is the design of a memoryless state feedback controller such that the closed-loop system is mean-square asymptotically stable for all admissible uncertainties. In the robust H ∞ control problem, in addition to the mean-square asymptotic stability requirement, a prescribed H ∞ performance is required to be achieved. In terms of a linear matrix inequality, sufficient conditions for the solvability of these problems are proposed respectively; the expressions of desired state feedback controllers are given. An example illustrating the proposed approach is provided.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
The record
- Venue
- IEEE Transactions on Automatic Control
- Topic
- Stability and Control of Uncertain Systems
- Field
- Engineering
- Canadian institutions
- University of Alberta
- Funders
- not available
- Keywords
- Control theory (sociology)Robust controlMathematicsLinear matrix inequalityState (computer science)Norm (philosophy)Full state feedbackExponential stabilityStability theoryRobustness (evolution)Bounded functionController (irrigation)Mathematical optimizationControl systemComputer scienceControl (management)EngineeringNonlinear systemAlgorithm
- Has abstract in OpenAlex
- yes