Control and Tension Estimation of a Cable Driven Mechanism Under Different Tensions
Bibliographic record
Abstract
This paper proposes a method to estimate and compensate for the changes of cable tension in the control of cable driven mechanisms. Cable tension may depend on various factors, including mechanism design, fabrication and operation. In many systems it is also an adjustable parameter that affects the performance of the control system. An implementation of the unscented Kalman filter is used for the simultaneous estimation of the states and parameters of a cable driven mechanism. Changes in cable tension are captured in the estimated parameters which, along with system states, are used by a model predictive controller to generate appropriate control actions. The method is described and its effectiveness is shown for a single degree of freedom cable driven robot. In addition, the correlation between the cable tension and the estimated robot parameters provides a way of estimating the tension. It is shown that cable tension can be inferred from one of the estimated robot parameters, namely cable stiffness.
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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 | 0.000 |
| 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".