Commande neuro-floue du robot PUMA560 muni de moteurs à courant continu dans les deux espaces tâche et articulaire
Bibliographic record
Abstract
The work presented in this article concerns the neuro-fuzzy ordering of robot PUMA 560 provided with engines to D.C. current in two spaces, task and articular. The step suggested, in the space of task consists in using the theory of the differential geometry to uncouple the model from the robot to be controlled, thus a neuro-fuzzy structure is introduced into the diagram of order in order to readjust the parameters of the nonlinear controller proposed. As for articular space the structure of synthesized order is based on considerations of stability of Lyapunov of the system to order, thus the law of order which results from this is broken up into two objectives. The first consists in determining the system neuro-fuzzy, the desired optimal couple to apply to each articulation and the second the tension to be sent to the engines. The results of simulation are presented and analyzed to prove the efficiency of the two approaches.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".