Control-Based Tension Distribution Scheme for Fully Constrained Cable-Driven Robots
Bibliographic record
Abstract
Control inputs of fully constrained cable-driven parallel robots (CDPRs) are constrained by the positiveness of the cables tension, as cables merely apply tensile forces. The positive tension distribution (PTD) in CDPRs is usually guaranteed with iterative optimization techniques, utilizing the redundant actuation of the CDPR. The iterative nature of the conventional PTD limits their real-time application since the worst-case computation time of the iterative methods is not predictable. In addition, optimization methods are prone to model uncertainties. This article addresses the PTD problem in the fully constrained CDPRs with a control viewpoint. In the proposed approach, the PTD algorithm is an integral part of the controller, which explicitly generates positive values for the cables tension. To this aim, a saturation-type function is coupled with the controller, and its effect is compensated using a nonlinear disturbance observer. The stability of the proposed control scheme is also investigated in detail through Lyapunov’s second method, considering a nonsingular terminal sliding mode controller. Furthermore, the performance of the proposed methodology is compared with the conventional method for a six-degrees-of-freedom CDPR, in the presence of uncertainties. Finally, the effectiveness of the proposed control scheme is investigated through experiments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".