Novel adaptive robust L<sub>1</sub>‐based controllers for teleoperation systems with uncertainties and time delays
Bibliographic record
Abstract
Abstract Despite various proposed control schemes for uncertain bilateral teleoperation systems under time delays, optimally restricting the system's overshoot has remained an overlooked issue in this realm. For this aim, we propose two novel control architectures based on robust L1 theory, entitled position‐based adaptive L1 controller and transparent adaptive L1 controller, with the former focusing on position synchronization and the latter concerning system transparency. Since developing L1‐based controllers for nonlinear telerobotic systems encompassing uncertainty and round‐trip delays puts significant theoretical challenges forward, the main contribution of this paper lies in advancing L1 theory within the field of delayed teleoperation control. To formulate the theories, the asymptotic stability of the closed‐loop system for each controller is first proved utilizing the Lyapunov method, followed by transformation, along with the L1 performance criterion, into linear matrix inequalities. Ultimately, the control gains are attained by solving a convex optimization problem. The superiority of the designed controllers over a benchmark transparent controller for teleoperators is demonstrated via simulation. Furthermore, experimental tests on a two‐degrees‐of‐freedom nonlinear telerobotic system validate the efficient performance of the proposed 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".