An intelligent sliding mode controller for vibration suppression in flexible structures
Bibliographic record
Abstract
A novel sliding mode (SM) control system with an embedded neuro-fuzzy approximator is developed in this paper to provide more effective vibration suppression, especially in flexible structures. It aims to force system state to move to, and maintain on, the defined sliding surface without chattering. A new hybrid training technique based on an extended gradient method is proposed to optimize the neuro-fuzzy system to approximate unknown nonlinear functions and to enhance control performance. When the principle of the terminal attractor is incorporated into the classical gradient method and/or SM control systems, some implementation problems arise especially when the error is close to its origin. The proposed extended gradient method can enhance the SM control to not only speed up convergence but also overcome the existing implementation problems of the terminal attractor. The Lyapunov stability analysis demonstrates that the approximation with the proposed hybrid training technique is stable and can converge to the optimal approximation. The effectiveness of the developed control system and the hybrid training technique is verified experimentally corresponding to nonlinear and time-varying system control.
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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 | 0.000 |
| 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) | 0.001 | 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".