Stable Nonlinear Trilateral Impedance Control for Dual-User Haptic Teleoperation Systems With Communication Delays
Bibliographic record
Abstract
A new nonlinear adaptive impedance-based trilateral controller is proposed to ensure the absolute stability of multi-degrees-of-freedom (DOFs) dual-user haptic teleoperation systems subjected to communication delays. Using this strategy, reference impedance models are realized for the trilateral teleoperation system represented by a three-port network to facilitate cooperation of two human operators in order to perform a remote physical task. For this purpose, an impedance model defines the desired haptic interaction between the two human operators, while another impedance model specifies the desired behavior of the slave robot in terms of tracking the mater robots' trajectories during interaction with the remote environment. It is shown that different performance goals such as position synchronization and force reflection can be achieved via different adjustments to the impedance parameters. The sufficient conditions for the trilateral haptic system's absolute stability are investigated in terms of the impedance models' parameters. Accordingly, guidelines for modification of the impedance parameters are obtained to guarantee the absolute stability of the trilateral haptic system in the presence of communication time delays. A trilateral nonlinear version of the model reference adaptive impedance control (MRAIC) scheme is developed for implementing the proposed reference impedance models on the masters and the slave. The convergence of robots' trajectories to desired responses and the robustness against modeling uncertainties are ensured using the proposed controller as proven by the Lyapunov stability theorem. The proposed impedance-based control strategy is evaluated experimentally by employing a nonlinear multi-DOFs teleoperated trilateral haptic system with and without communication delays.
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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 | 0.000 | 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".