Experimental results for output feedback adaptive robot control
Bibliographic record
Abstract
This paper examines three methods of adaptive output feedback control for robotic manipulators. Implementing output feedback control allows use of only the position information, which can be measured quite accurately. Velocity and acceleration measurements can get corrupted by noise. A method proposed by K. W. Lee and H. K. Khalil [Adaptive output feedback control of robot manipulators using high-gain observer, Int. J. Control, 6, 869–886 (1997)] using a high-gain observer, one proposed by J. J. Craig, P. Hsu and S. S. Sastry [Adaptive control of mechanical manipulators, Int. J. Robot. Res., 6(2), 16–27 (1987)] with the addition of a linear observer that we propose, and a method proposed by R. Gourdeau and H. M. Schwartz [Adaptive control of robotic manipulators: Experimental results, Proceedings of the 1991 IEEE International Conference on Robotics and Automation (Apr. 1991) pp. 8–15] using an Extended Kalman Filter are examined. The methods are implemented in simulation and experimentally on a direct-drive robot. The performance of each of the algorithms is compared.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.010 | 0.002 |
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".