Bibliographic record
Abstract
The unifying idea for most model-based control approaches for parallel mechanism is to derive a minimal-order dynamics model of the system and then design the corresponding controller. The problem with such a control approach is that the controller needs to change its structure whenever the mechanical system changes its number of degrees-of-freedom. This paper presents a projection-based control scheme for parallel mechanism that works whether the system is overactuated or not; it does not require derivation of the minimal-order dynamics model. Since the dimension of the projection matrix is fixed, the projection-based controller does not need to change its structure whenever the mechanical system changes its number of degrees-of-freedom. The controller also allows to specify lower and upper bounds on the actuator forces/torques, making it suitable not only for the control of parallel manipulators with limited force/torque capability of the actuators but also for backlash-free control of parallel manipulators as well as for control of tendon driven parallel manipulators. The stability of the projection-based controllers is rigourously proved, while the condition for the controllability of parallel manipulators is also derived in detail. Finally, experimental results obtained from a simple parallel mechanism, which changes its degrees-of-freedom, are appended. The results also demonstrate that the maximum actuator torque can be reduced by 20% if the actuator saturation is taken into account by the controller.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".