Fuzzy bilateral control of time delayed nonlinear tele-robotic system in unknown environments through state convergence
Bibliographic record
Abstract
State convergence belongs to the class of non-passive schemes and offers a complete framework for bilaterally controlling the tele-robotic systems. Contrary to many other schemes, it allows modeling the tele-robotic systems on state space and provides guaranteed performance of their closed loop behavior. In this study, we have used the state convergence scheme to design a bilateral controller for a nonlinear tele-robotic system where the slave is working in an unknown environment. The nonlinear tele-robotic system is first approximated by a Takagi-Sugeno (TS) fuzzy model. A fuzzy control law is then employed to derive the design conditions following the method of state convergence in order to ensure that the slave follows the master and the desired dynamic behavior of the tele-robotic system is achieved. Further, the existing state convergence based linear bilateral controller is found to be a special case of the proposed state convergence based fuzzy bilateral controller. A one-degree-of-freedom (DoF) nonlinear tele-robotic system is finally simulated in MATLAB/Simulink environment to show the effectiveness of the proposed approach.
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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 | 0.001 |
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".