Robot Manipulator Control for Rigid and Assumed Mode Flexible Dynamics Models
Bibliographic record
Abstract
rigid Euler-Lagrange dynamics model but are extended here to a flexible dynamics model based on the first assumed mode calculated using Euler-Bernoulli pinnedpinned beam theory. A square trajectory presents an ideal case for studying intense vibration effects and their control at the four orthogonal direction switches. 7 Operational problems with robots in space relate to several factors. One most important is the structural flexibility problem of a robot manipulator and subsequently significant difficulties with its control systems, especially, position control. Typically, small elastic vibrations of the links coupled with their large rigid link rotations and non-linear dynamics primarily cause these difficulties. This paper presents a control scheme for positioning the endpoint of a two-link robot manipulator modeled with assumed modes flexible dynamics while tracking a square trajectory. The dominant assumed mode vibration is derived for an Euler-Bernoulli pinned-pinned beam to model link flexibility then, coupled with nonlinear dynamics for large rotations of rigid links to formulate an EulerLagrange inverse dynamics robot model. A control law derived using the Jacobian transpose provides joint actuation for both rigid and flexible dynamics. Comparison of output trajectories shows some differences in control performance between the rigid and flexible dynamics models with the transient response characteristics demonstrating a reduction in maximum overshoot for the flexible dynamics over the rigid dynamics models but a lengthier settling time with a consistent albeit slight oscillation about the entire desired steady-state ttrajectory .
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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 | 0.000 |
| 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".