FEEDFORWARD CONTROL OF FLEXIBLE LINK SYSTEMS USING PARALLEL SOLUTION SCHEME
Bibliographic record
Abstract
In this paper, a parallel solution scheme of inverse dynamics is revised and applied to flexible link systems where elastic deformation and vibration normally occur in constituting links. The scheme is considered to be valid for link systems with elastic members, as the calculation process of the scheme is based upon a finite element approach. It evaluates the analysed model in absolute Cartesian coordinates with the equation of motion expressed in dimension of force. The calculated nodal forces are converted into joint torques using a matrix form equation divided into terms of force, transformation between coordinates, and length. Therefore, information from the entire system can be handled in parallel, which makes the calculation seamless in application to any type of link system regardless of its boundary conditions or stiffness values. In this paper, the scheme is revised and the calculation time is shortened by applying Bernoulli-Euler beam elements, and it is then combined with a kinematics solution scheme that calculates target trajectories for flexible models. The calculation flow of inverse dynamics is shown for a five-link system, and some feedforward control experiments are carried out on a two-link system with different stiffness links. The accuracies of trajectories and torque values are verified by applying the system to a sensorless, model-based vibration control. The trajectories and torque values are confirmed to be highly accurate compared with the actual data for feedforward control, and the validity of the approach is verified.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".