Position synchronization for an uncertain teleoperation system with time delays using L1 theory
Bibliographic record
Abstract
The problem of position tracking in teleoperation systems containing latencies and dynamical uncertainties is addressed in this work. In many applications, such as telesurgery, safe interaction with the external environment is a factor which may undermine the synchronization of the positions. For nondestructive contact with the environment, in addition to an errorless steady-state position tracking, the closed-loop system requires to have a response with the least possible overshoot. To this end, a state-feedback controller based upon L1 theory is proposed in this work. The compensator is synthesized utilizing the linear matrix inequality (LMI) technology, and the asymptotic stability of the system is verified employing Lyapunov-Krasovskii functional. Another advantage of the proposed control scheme is that it is robust to asymmetric randomly varying time delays in the communication channels. The L1-based controller is finally compared to the well-known sliding mode controller via simulation, and is proved to outperform it from maximum error point of view, while preserving low steady-state error. The proposed controller is also illustrated to be effective even in the presence of model uncertainties.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".