Adaptive Synchronized Control for a Planar Parallel Manipulator: Theory and Experiments
Bibliographic record
Abstract
In this paper, we develop a new control method, termed adaptive synchronized (A-S) control, for improving tracking accuracy of a P-R-R type planar parallel manipulator with parametric uncertainty. The novelty of A-S control, a combination of synchronized control and adaptive control, is in the application of synchronized control to a single parallel manipulator so that tracking accuracy is improved during high-speed, high-acceleration tracking motions. Through treatment of each chain as a submanipulator; the P-R-R manipulator is thus modeled as a multi-robot system comprised of three submanipulators grasping a common payload. Considering the geometry of the platform, these submanipulators are kinematically constrained and move in a synchronous manner. To solve this synchronization control problem, a synchronization error is defined, which represents the coupling effects among the submanipulators. With the employment of this synchronization error, tracking accuracy of the platform is improved. Simultaneously, the estimated unknown parameters converge to their true values through the use of a bounded-gain-forgetting estimator. Experiments conducted on the P-R-R manipulator demonstrate the validity of the approach.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".