Dynamic modeling and computed torque control of a 3-DOF spherical parallel manipulator
Bibliographic record
Abstract
This paper investigates the dynamic modeling of a 3-degree-of-freedom spherical parallel manipulator, called the Agile Eye, first build at Laval University. The approach used in this paper is based on detaching the manipulator into several subsystems and applying a consecutive synergy between kinematic analysis, Lagrangian and Newtonian approaches. In this regard, the manipulator under study is detached to four subsystems. After writing down the kinematic equations of all the three subsystems, the Lagrangian and Newtonian approaches are blended and finally the dynamic model of the 3-DOF Agile Eye is obtained. Finally, the problem leads to a system of 12 equations and 18 unknowns, which has been simplified to have a fully constraint system of equations. The results are put into contrast by the one obtained with a analyser software, MD-Adams. Then a co-simulation between MATLAB and MD-Adams has been accomplished in order to control the Agile Eye with computed torque control method. The latter method has led to the end-effector (EE) to follow the desired trajectory perfectly.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".