Stability guaranteed teleoperation: an adaptive motion/force control approach
Bibliographic record
Abstract
An adaptive motion/force controller is developed for unilateral or bilateral teleoperation systems. The method can be applied in both position and rate control modes, with arbitrary motion or force scaling. No acceleration measurements are required. Nonlinear rigid-body dynamics of the master and the slave robots are considered. A model of the flexible or rigid environment is incorporated into the dynamics of the slave, while a model of the human operator is incorporated into the dynamics of the master. The master and the slave are subject to independent adaptive motion/force controllers that assume parameter uncertainty bounds. Each parameter is independently updated within its known lower and upper bounds. The states of the master (slave) are sent to the slave (master) as motion/force tracking commands instead of control actions (efforts and/or flows). Under the modeling assumptions for the human operator and the environment, the proposed teleoperation control scheme is L/sub 2/ and L/sub /spl infin// stable in both free motion and flexible or rigid contact motion and is robust against time delays. The controlled master-slave system behaves essentially as a linearly damped free-floating mass. If the parameter estimates converge, the environment impedance and the impedance transmitted to the master differ only by a control-parameter dependent mass/damper term. Asymptotic motion (velocity/position) tracking and force tracking with zero steady-state error are achieved. Experimental results are presented in support of the analysis.
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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".