Discrete-time dynamic event-triggered state observers for second-order systems and their applications to a quarter-car suspension system
Bibliographic record
Abstract
This paper considers the problem of designing discrete-time dynamic event-triggered state observers for second-order systems with external disturbances. The obtained theoretical results are applied to estimate the state position and velocity vectors of a quarter-car suspension system. For the first time, state vectors of second-order systems can be robustly estimated by using a discrete-time dynamic event-triggered state observer. Unlike existing methods of designing state observers for second-order systems, which are based on Luenberger state observers, the one in this paper is a natural second-order observer. Moreover, the proposed natural second-order observer uses only information from the output vector when a discrete supervision holds. This indicates that the utilization of communication resources is reduced while maintaining the desired robust estimation performance. In addition, different from the existing method based on a parameter-dependent Lyapunov function in investigating the stability of the dynamic error system of natural second-order observers, a convex optimization problem is established in this paper to give a minimized level in evaluating the boundedness of the dynamic error system. First, a new discrete-time dynamic event-triggered natural second-order observer is designed. Then, a sufficient condition for the existence of such an observer is established. Finally, the obtained results are applied to the quarter-car suspension system.
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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 | 0.002 |
| 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 | 0.001 | 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".