Disturbance observer based adaptive robust control of bilateral teleoperation systems under time delays
Bibliographic record
Abstract
Bilateral teleoperation technology has caused wide attentions due to its applications in various remote operation systems. However, to really realize teleoperation requirements, there still exist some challenging control issues: a) the communication delay between master and slave manipulators may lead to system instability or performance decreasing; b) in some applications, the sensors are not easily set up to measure the environmental external force; c) various manipulator modeling uncertainties need to be considered carefully in order to achieve good control performance. In this paper, the disturbance observer is designed based on the slave manipulator dynamics to observe the unmeasurable environmental force. When the environmental force is modeled 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. This design can avoid solving the complicated passivity problem under communication delays and having the trade-off between the system stability and control performance, and thus has the potential of achieving the excellent control performance and the guaranteed robust stability simultaneously under arbitrary time delays. The sliding mode control and the force compensation of disturbance observer are integrated subsequently to deal with various manipulator modeling uncertainties, so that the excellent synchronization performance can be achieved. The simulation on single DOF manipulators is carried out and the results show the effectiveness of the proposed control algorithm.
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 | 0.001 | 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".