Adaptive Finite-Time Coordination Control of a Multi-robotic Fiber Placement System With Model Uncertainties and Closed Architecture
Bibliographic record
Abstract
The coordination and trajectory tracking accuracy of multi-robotic fiber placement systems (MRFPSs) are critical to assure the quality of the fiber placement process. However, the model uncertainties and closed architecture (CA) in industrial robots significantly hinder the system from achieving high performance in coordination and tracking simultaneously. In addition, the convergence rates of the tracking and synchronization errors are also essential performance indicators for the MRFPSs. To improve the three abovementioned performances, this article presents an equivalent model of the CA dynamics based on a radial basis function neural network. Employing this equivalent model, a novel indirect torque control algorithm named adaptive finite-time coordination control (AFCC) is proposed for a MRFPS consisting of two heterogeneous robots. Within the controller, two adaptive laws are designed to handle the uncertainties, and three additional adaptive laws are developed to mitigate the effects of the unknowns in the CA, contact forces, and disturbances. The stability analysis of the AFCC algorithm proves that the errors can converge to zero within a finite time. Furthermore, three experiments show the advantages and practicality of the AFCC algorithm.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".