Asymptotically stable robust control of robot manipulators
Why this work is in the frame
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Bibliographic record
Abstract
Most robust control methods of robot manipulators guarantee small tracking errors by applying sufficiently high feedback gains. Infinite gains are required for zero tracking errors. However, in practice, feedback gains could be severely limited by hardware factors. Robust control schemes using low feedback gains are desirable. In this paper, we derive a globally asymptotically stable robust control scheme by combining integral control with a robust saturation control law. The proposed robust control method takes advantage of both saturation control and integral control techniques, while the disadvantages attributed to them are remedied by each other. Globally asymptotic stability of an n-link robot system with parametric model uncertainties is achieved. As an illustration, the proposed control scheme is applied to a two degrees-of-freedom direct drive robot arm. Simulations were conducted, and the results are in accordance with the theoretical analysis.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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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) | 0.001 | 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 it