Adaptive synchronization control of a planar parallel manipulator
Bibliographic record
Abstract
A new control algorithm for a planar parallel robotic manipulator with three degrees-of-freedom (DOF) and parametric uncertainties has been developed. From its mechanical structure, the studied planar parallel manipulator is categorized as a P-R-R type and can be treated as comprised of three constrained submanipulators. Key to the successful trajectory tracking control of the P-R-R manipulator is the motion of the submanipulators: each sub-manipulator should be controlled to follow its pre-determined trajectory while coordinating motions with the other sub-manipulators. The control algorithm developed employs the above idea and incorporates synchronization technology with the adaptive control architecture by feeding back position, velocity errors of the actuated joints and a newly defined synchronization error. Employment of the synchronization error, verified by simulations, substantially reduces the pose error of the end-effector of the P-R-R manipulator during trajectory tracking. From theoretical analysis, the proposed control algorithm is shown to guarantee the convergence of tracking errors and the synchronization error at the same time. Finally, simulation results demonstrate that the proposed controller can achieve excellent trajectory tracking performance.
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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) | 0.001 | 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 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".