Parameter Identification and Adaptive Control Of Carbon Nanotube Resonators
Bibliographic record
Abstract
Abstract In this paper, we exploit an adaptive control scheme to adjust highly sensitive oscillations of fluid conveying carbon nanotube (CNT) resonators. Firstly, we focus on the nonlinear vibrations of the fluid conveying CNTs, considering an added mass using nonlocal Euler‐Bernoulli beam theory. CNT rests on nonlinear Winkler and Pasternak foundations. We use the Galerkin method to extract the nonlinear ordinary differential equation models of the CNT oscillations. We elicit a linear parametric model for estimating the added mass and other parameters of the system. Numerical simulations delineate that the developed model has sensitivity to added mass at the yoctogram level. It is known that CNT vibrations are very sensitive to small perturbations. Accordingly, a small perturbation results in significantly abrupt changes in the vibrational parameters of the targeted system. For that reason, it is crucial to have a potent apparatus for identifying the system parameters in case of sudden changes in the vibrational parameters. For such parameter identification, a least squares (LS) parameter identification algorithm and an extended Luenberger observer are integrated to a pole placement controller for online estimation of the system parameters as well as vibration control of the objective system. It is well‐known that CNTs are potentially ideal atomic force microscopy (AFM) probes, and accordingly, the proposed method is potentially beneficial for identifying highly sensitive motions in AFM. In addition, numerical simulations are presented, showing that the proposed adaptive controller has the potential to be used for vibration control of the CNT resonators even in the case of chaotic motions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".