Adaptive robust control of bilateral teleoperation systems with unmeasurable environmental force and arbitrary time delays
Bibliographic record
Abstract
Bilateral teleoperation technology has caused wide attentions because of its applications in various remote operation systems. However, there exist some challenging control issues such as communication delay, unmeasurable environmental force, and various manipulator modelling uncertainties. In this study, the disturbance observer is designed based on the slave manipulator dynamics to observe the unmeasurable environmental force. When the environmental force is modelled as a general linear regression form, its unknown parameters can be estimated online by the least square adaptation law. A novel communication structure is proposed where only the master trajectory is transmitted to the slave side, and the transmission signal from the slave to the master is replaced by those estimated environmental parameters. Since these parameters are not power signals, the passivity problem of the communication channel and the trade‐off limitation between the transparency performance and robust stability in traditional teleoperation control are essentially avoided. The sliding mode control and the force compensation of disturbance observer are integrated subsequently to deal with various manipulator modelling uncertainties, so that the excellent synchronisation performance can be realised. Thus, the proposed control algorithm can guarantee the robust stability and the good control performance simultaneously under arbitrary time delays. The simulation and experiment on two single degree‐of‐freedom manipulators are carried out and the results show the effectiveness of the proposed control algorithm.
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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 | 0.001 | 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 | 0.001 | 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".