Adaptive sliding mode observers for sector‐bounded nonlinear systems
Bibliographic record
Abstract
Summary This article introduces a novel sliding mode observer design for sector bounded nonlinear systems. The proposed observer can simultaneously estimate both states and unknown parameters in the presence of disturbances and measurement noise. The observer is developed by using a time‐averaged Lyapunov (TAL) functional to analyze the effect of noise (Gaussian) and to adequately reduce its effect on the system. The TAL averages the Lyapunov analysis over a small finite time interval, allowing for intuitive analysis of noises and disturbances acting on the system. The TAL is shown to satisfy all the requirements of a Lyapunov candidate function. The article focuses on the observer design for sector bounded nonlinear systems since several nonlinearities can be modeled using a sector bound. An optimization approach is also developed to provide a tight bound on the effect of the uncertainty on the estimated parameters. The conditions for the existence of the observer are presented in the form of linear matrix inequality (LMI), which can be explicitly solved offline using commercial LMI solvers. Furthermore, the LMI design is also extended to a specific case of a dissipative nonlinear system. The observer design has also been extended to the case where the input disturbance is correlated with the sensor noise.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".