Non-Linear Adaptive Output Feedback Control of Robot Manipulators
Why this work is in the frame
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Bibliographic record
Abstract
This paper examines three methods of adaptive output feedback control for robotic manipulators. Implement ing output feedback for control, instead of full-state feedback, allows use of only the position information. The position can be measured quite accurately, while velocity and acceleration measurements tend to get corrupted by noise. As well, having only a position sensor reduces costs in producing the robot. The three methods examined each use some form of state estimation. The methods examined are: a method proposed by Lee and Khalil using a high-gain observer, Craig, Hsu, and Sastry’s method of adaptive robot control using a linear observer that we propose herein, and a method proposed by Gourdeau and Schwartz using an Extended Kalman Filter (EKF). The methods are all implemented in simulation and compared in both noise-free and noise-contaminated cases.
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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)
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