Fuzzy Model-based Design of a Transparent Controller for a Time Delayed Bilateral Teleoperation System Through State Convergence
Bibliographic record
Abstract
Transparency optimized state convergence scheme is a simple, elegant and easy to design bilateral control algorithm for n th -order linear teleoperation systems modeled on state space.It requires the solution of 3n+2 design equations to determine the control gains when the time delay in the communication channel is small.The controller thus obtained allows the slave system to follow the master system's motion in a desired dynamic way while providing the human operator with a high degree of environment's feeling during the steady state operation.This paper employs the transparency optimized state convergence scheme to design a bilateral controller for a class of nonlinear teleoperation systems which can be approximated by Takagi-Sugeno (TS) fuzzy models.To close the feedback loop around master and slave systems, a suitable parallel distributed compensation (PDC) type TS fuzzy controller is selected which allows to use the state convergence procedure in a true sense.In this way, all the benefits of the state convergence scheme have been kept intact while extending its applicability to nonlinear teleoperation systems at the same time.Further, the proposed fuzzy state convergence controller is more general as the existing linear state convergence controller can be derived from it.MATLAB simulations on a onedegree-of-freedom (DoF) nonlinear teleoperation system are included to show the efficacy of the proposed fuzzy transparent state convergence controller.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".