Extended fuzzy logic controller for uncertain teleoperation system
Bibliographic record
Abstract
Teleoperation systems allow a surgeon to perform a remote distance operation with or without magnification. Nonlinearity and unknown dynamics of master and slave robots, in teleoperation systems, make it challenging to guarantee stability and convergence of the position tracking error in such systems. This paper presents an interval Type-2 Fuzzy (T2F) logic controller to control position of a teleoperation system without knowing dynamics of master and slave robots. Interval T2F controller have been recently applied in many engineering fields while understanding the control potentials of interval T2F still have been an open question for researches. The control methodology is baseline optimized Type-1 Fuzzy (T1F) Controllers. Although, the performance of T1F controller, deals with unknown dynamics, is reasonable, but in comparison to interval T2F controller, when uncertainties are increased, it has weaker performance. The performance of the controller has been evaluated on the test — bed consists of two Novint Falcon robots as the master and slave. The experimental results show improved performance of interval T2F controllers in comparison to T1F controllers.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".