Second-Order Sliding Fuzzy Interval Type-2 Control for an Uncertain System With Real Application
Bibliographic record
Abstract
A new second-order sliding-mode type-2 fuzzy controller for nonlinear uncertain perturbed systems is developed in this paper. To overcome the constraint on the knowledge of the system model, we have used local models that are related to some operating points to synthesize a type-2 nominal fuzzy global model. The control is based on the super-twisting algorithm, which is among second-order sliding-mode controls (SMCs). Moreover, two adaptive fuzzy type-2 systems have been introduced to generate the two super-twisting signals to avoid both the chattering and the constraint on the knowledge of disturbances and uncertainties upper bounds. These adaptive fuzzy type-2 systems are adjusted online by two adaptation laws that are deduced from the stability analysis in the Lyapunov sense. It has only one input, i.e., the sliding surface, and one output, i.e., the optimal values of the gains control, which are hard to compute with the original algorithm. Many results of the one-link manipulator are obtained: first by the simulation in order to compare the performances of the proposed method with that given by Levant and then in a real-time application in order to confirm the efficiency of the proposed approach. The experimentation and simulation are done for the tracking control problem.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".