A novel adaptive robust control architecture for bilateral teleoperation systems under time‐varying delays
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Bibliographic record
Abstract
Summary Bilateral teleoperation technology has caused wide attentions due to its applications in various remote operation systems. The communication delay becomes one of the main challenging issues in the teleoperation control design. Meanwhile, various nonlinearities, parameter variations, and modeling uncertainties existing in manipulator and environment dynamics need to be considered carefully in order to achieve good control performance. In this paper, a globally stable nonlinear adaptive robust control algorithm is developed for bilateral teleoperation systems to deal with these control issues. Namely, the unknown dynamical parameters of the environmental force are estimated online by the improved least square adaptation law. A novel communication structure is proposed where only the master position signal is transmitted to the slave side for the tracking design, and the online estimators of the environmental parameters are transmitted from the slave to the master to replace the traditional environmental force measurement. Because the estimated environmental 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 nonlinear adaptive robust control is subsequently developed to deal with nonlinearities, unknown parameters, and modeling uncertainties of the master, slave, and environmental dynamics, so that the guaranteed transient and steady‐state transparency performance can be achieved. The experiments on two voice‐coil motor‐driven manipulators are carried out, and the comparative results verify that the proposed control algorithm achieves the excellent control performance and the guaranteed robust stability simultaneously under time delays. Copyright © 2014 John Wiley & Sons, Ltd.
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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)
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Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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