Dynamic Model-Free Control Approach for Fully Constrained Cable-Driven Parallel Robots: Prescribed Control Range
Bibliographic record
Abstract
Cable-driven parallel robots (CDPRs) utilize cables instead of rigid linkages. Considering the complexity of the cable dynamics and the positive constraint on the cable tension, the precise tracking control of CDPRs is highly challenging and of great significance. The positive tension distribution (PTD) for the CDPRs has been conventionally done via redundancy resolution methods which are optimization based in nature. Hence, the unpredictable worst-case computation time of these methods limits their practical applications. Moreover, a performant tracking control traditionally requires an exact knowledge of the cable dynamics which is practically hard to obtain for CDPRs due to their complicated dynamics. To remedy these challenges, we propose a real-time dynamic model-free PTD scheme for the redundant CDPRs. This control structure brings two major benefits to the control of redundant CDPRs. First, the elapsed time of the analytic proposed method is considerably less than that of the iterative literature methods. Second, high-precision tracking accuracy is achieved despite the challenging cable dynamics and the presence of external disturbance. It is owing to the dynamic model-free structure of the controller. Eventually, the performance analysis is assessed through the simulation and experiment, verifying the efficiency of the proposed method.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".