A Self-Evolving Neural Network-Based Finite-Time Control Technique for Tracking and Vibration Suppression of a Carbon Nanotube
Bibliographic record
Abstract
The control of micro- and nanoscale systems is a vital yet challenging endeavor because of their small size and high sensitivity, which make them susceptible to environmental factors such as temperature and humidity. Despite promising methods proposed for these systems in literature, the chattering in the controller, convergence time, and robustness against a wide range of disturbances still require further attention. To tackle this issue, we present an intelligent observer, which accounts for uncertainties and disturbances, along with a chatter-free controller. First, the dynamics of a carbon nanotube (CNT) are examined, and its governing equations are outlined. Then, the design of the proposed controller is described. The proposed approach incorporates a self-evolving neural network-based methodology and the super-twisting sliding mode technique to eliminate the uncertainties’ destructive effects. Also, the proposed technique ensures finite-time convergence of the system. The controller is then implemented on the CNT and its effectiveness in different conditions is investigated. The numerical simulations demonstrate the proposed method’s outstanding performance in both stabilization and tracking control, even in the presence of uncertain parameters of the system and complicated disturbances.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".