Minimum-norm Solution for the Actuator Forces in Cable-based Parallel Manipulators based on Convex Optimization
Bibliographic record
Abstract
Cable-based parallel manipulators (CPM) are light-weight manipulators that can reach high accelerations. The difference between the design of CPM and that of rigid-link parallel manipulators is that cables can only perform while under tension. Redundant limbs, such as extra cables, springs, or cylinders, can be used for applying forces on the mobile platform to generate cable tensions resulting in a redundantly actuated manipulator. To operate this manipulator, the actuator-force distribution amongst the cables and the redundant limbs needs to be determined. Actuator-force optimization techniques developed for rigid-link manipulators are unsuitable for CPM. In this study, a numerical procedure based on convex analysis and optimization is presented to calculate the minimum-norm solution that minimizes the 2-norm of actuator forces. The procedure is based on convex optimization that utilizes the Dykstra's alternating projection algorithm to reach to the optimum solution. This numerical method is successfully applied to 3- and 6-degree-of-freedom (DOF) spatial CPMs to determine the optimum actuator forces for a given external load. This study addresses the static analysis in cable-based parallel manipulators in the language of convex analysis
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".