Adaptive Distributed Boundary Vibration Control of Multiagent Euler–Bernoulli Beams via Cooperative Disturbance Observer Network
Bibliographic record
Abstract
This article presents a method for the vibration suppression problem of a network of multiagent Euler–Bernoulli beams whose dynamics are governed by fourth-order partial differential equations (PDEs). Particularly, the considered multiagent systems are subjected to unknown external disturbances causing unexpected vibration. To this end, this article develops an adaptive vibration controller to reject unknown disturbances and achieve vibration suppression. The proposed controller is equipped with a novel network of cooperative boundary disturbance observers, and each observer in the network transmits the estimated disturbance information. The cooperation among the observers in the network guides to achieve observation consensus. Moreover, based on the proposed disturbance observer network, a new antivibration adaptive boundary controller is developed, and the closed-loop stability is proved based on Lyapunov theory. In addition, it is also shown theoretically that the proposed controller is robust to unknown spatiotemporally distributed load. To validate the effectiveness of the proposed method, numerical simulation examples are carried out, and the application on a marine riser system is studied to further show the strength of the proposed method.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".