Smith Predictor Type Control Architectures for Time Delayed Teleoperation
Why this work is in the frame
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Bibliographic record
Abstract
An early control methodology for time delayed plants is the Smith predictor, in which the plant model is utilized to predict the non-delayed output of the plant and move the delay out of the control loop. Recent Smith predictor based teleoperation control architectures have used linear or fixed-parameter dynamic approximations of the slave/environment at the master for environment contact prediction. This paper discusses and analyzes the performance of the previous work and proposes new architectures to overcome their shortcomings. The proposed architectures consist of a novel pseudo two-channel nonlinear predictive controller and its variations that use neural networks for online estimation of the slave and environment dynamics to replicate the environment contact force at the master using a similar local network. Intermittent contact experiments are conducted on a teleoperation test-bed consisting of two Planar Twin-Pantograph haptic devices. The experimental results with half a second delay demonstrate significant improvement in stability and performance by the proposed neural network based predictive control architectures over traditional force-position and linear Smith predictor based control architectures.
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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 | 0.001 | 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 it