Comparison of PD-Based Controllers for Robotic Manipulators
Why this work is in the frame
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Bibliographic record
Abstract
PD control is widely used in industrial robotic manipulators because of its simple structure and acceptable performance. In this paper, the PD-based control schemes for the trajectory tracking of the robotic manipulators are addressed. The fixed gain PD control, the nonlinear gain PD (NPD) control, the adaptive PD learning control (PD-LC), and the adaptive NPD learning control (NPD-LC) are applied for the trajectory tracking of both serial and parallel robotic manipulators. The PD-LC and NPD-LC controllers can be used to improve the tracking performance for the repeatable tracking tasks in an iterative mode. The PD-LC and NPD-LC consists of a PD/NPD control as the basic feedback control and an additional feedforward control term directly inherited from the previous iteration of the same control task. A comparative study of four PD-based controllers is conducted to understand how different control schemes will affect the trajectory tracking performance, and the results are shown in this paper. Case studies are presented to demonstrate the validity of the PD-LC and NPD-LC algorithms.
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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 it