Compensation of sampled-data robot adaptive controllers for improved stability
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Bibliographic record
Abstract
This paper addresses the stability and performance of discretized adaptive control algorithms for robotic manipulator control, and the compensation of these algorithms for improved stability and tracking performance. The discretization of Slotine and Li's (1987, 1988) direct adaptive control algorithm results in a sampled-data system for which stability has not been guaranteed. By formulating the entire sampled-data system in continuous-time, Lyapunov's direct method is used to examine the stability bounds. A proposed nonlinear discrete-time compensating term is added to Slotine and Li's adaptive algorithm, to stabilize the sampled-data system. For sufficiently high gain, globally stable performance and a known bound on the norm of the filtered error is proven. A multi-rate discrete-time controller implementation of the compensated adaptive controller is investigated. The stabilizing effect of the compensator is demonstrated through simulation of a 2 degree-of-freedom serial link manipulator.< <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) | 0.000 | 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