On the gyroscopic force in mechanical manipulators and its artificial shaping for taskspace movement coordination
Bibliographic record
Abstract
In this paper, we try to draw attention to a particular type of force that has been somehow overlooked in the control of mechanical manipulators: gyroscopic force. At first, we broaden our understanding of gyroscopic effect intrinsic in mechanical systems by reformulating the Euler-Lagrange (EL) equation in terms of a gyroscopic part and the non-gyroscopic ones (or Rayleigh type). Then, we propose the use of artificial gyroscopic force to assist existing control laws. As a force that does not do any work, the gyroscopic force can be effectively used in combination with many existing control laws for mechanical systems without affecting their salient features such as the stability or the convergence to a target point. As a specific example, we look into the potential energy shaping parameterized by taskspace variables (also known as the Jacobian transpose method) as a base control law, and combine it with an artificial gyroscopic forcing term. We propose one specific way to design the gyroscopic force as a term quadratic in velocity that operates in reference to a desired velocity field shape (also parameterized in taskspace). The resulting control strategy enables us to shape the intermediate path profiles and thus realizes a taskspace control law that is free from any inverse transformations. We demonstrate the effectiveness of this method using simulation results with a three-link planar manipulator.
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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) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".