Backstepping control for stochastic nonlinear strict-feedback systems based on observer with incomplete measurements
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Bibliographic record
Abstract
In this paper, the control problem for a class of stochastic nonlinear systems with incomplete measurements is investigated based on Luenberger-like nonlinear state observer. The systems are described as strict-feedback cyber-physical systems, in which the communication between the control centre and the physical system is subjected to disturbances. The disturbances, such as information packet losing or transmission medium saturation, cause state variables to be unavailable or distorted, which results in incomplete measurements. To solve these problems, two state estimators are designed for different transmission cases, based on which two backstepping control approaches are adopted. The stability conditions of the state estimators and closed-loop system are derived. It is proved that the control methods can guarantee that all the signals of the closed-loop system are uniformly ultimately bounded in mean square. The effectiveness of the proposed methods is confirmed by simulation examples.
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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 | 0.000 | 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 | 0.000 |
| 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)
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