Periodic Event-Triggering Adaptive Control for Networked Uncertain Nonlinear Systems Against Actuator Attacks and Its Applications
Bibliographic record
Abstract
This article proposes a sampled-data event-triggered adaptive neural network (NN) control strategy to cope with the digital communication and attack compensation problems of networked systems with actuator attacks and exogenous disturbance. By combining event triggering state, parameter estimation signals, and disturbance observer, a novel digital state feedback controller is designed to reduce its updating frequency and compensate for the deliberate impact of unknown actuator attacks. Moreover, considering that the state is partially measurable, a novel observer-based digital controller is designed via a double-ended event-triggering mechanism (ETM). Then, two new Lyapunov functionals are created to analyze the system stability, and two design methods are given to solve the control gain. Finally, the feasibility and validity of the derived results are verified by a visual servo control system and an offshore structure system.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".