Disturbance-Observer-Based Sliding Mode Control Design for Nonlinear Bilateral Teleoperation System With Four-Channel Architecture
Bibliographic record
Abstract
Good transparency performance (e.g., position tracking and force feedback) is an important issue in the control design of teleoperation systems. The four-channel architecture has advantages in the achievement of good transparency performance for teleoperation systems. However, most of the existing four-channel architectures are used in the linear teleoperation system, which cannot be well applied to execute tasks in the increasingly complicated operation environments. Thus, designing the four-channel architecture to achieve good transparency performance for nonlinear teleoperation systems with uncertainties is still a challenging issue. In this paper, the disturbance-observer-based sliding mode control design with a novel four-channel architecture is developed for the nonlinear bilateral teleoperation system to achieve a good transparency performance in the consideration of uncertainties and external disturbance, where the operating torque, master position, slave position, and the environment torque signals are transmitted through the communication channel. The reference tracking planner is designed in both the master and slave sides to produce the passive reference trajectories, and the disturbance-observer-based sliding mode controller is subsequently designed for the master and slave robots to achieve the good position tracking performance. The good force feedback performance can be achieved by the proper selection of plan parameters in the master reference tracking planner. Thus, the good transparency performance with both position tracking and force feedback can be achieved for the nonlinear bilateral teleoperation system with this novel four-channel architecture. The comparative simulation and experiment are implemented, and the results demonstrate good transparency performance achievement with the proposed control design.
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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".