Adaptive robust control for bilateral teleoperated robotic manipulators with arbitrary time delays
Bibliographic record
Abstract
Bilateral teleoperation systems have been extensively developed over decades. Communication delay is one of the most challenging control design problems. Additionally, nonlinearity, parameter variation, and uncertainty in the environment dynamics and robot model are also challenging issues that should be taken into account for excellent control performance. In our work, we propose a globally stable non-linear adaptive robust control structure is proposed, dealing with the following problems. Firstly, this new control structure can tolerate arbitrary, long, and time-varying delays. Secondly, a nonlinear adaptive robust control scheme is proposed to stabilize the system under nonlinearities, unknown parameters, modeling errors and uncertainties in the system, in order to ensure excellent tracking performance on both sides. Thirdly, an environmental torque estimator is designed to estimate immeasurable torques by a least square adaptive law. Moreover, a novel structure of communication block is developed. The master trajectory is sent to the slave side. However, from the slave side to the master side, estimated parameters of the environmental torque are transmitted back. This structure is designed to enhance the control performance of the adaptive robust controller. To ensure the desired transparency performance, an impedance control structure is developed on the master side. The proposed approach can guarantee robust stability, excellent transparency and synchronization under arbitrary time-varying delays. In simulation, two 2-DoF robotic manipulators are considered to verify the effectiveness of the control design.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".