Adaptive Nonlinear PD Learning Control for Robot Manipulators
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Bibliographic record
Abstract
A new control method, called adaptive nonlinear PD learning control (NPD-LC), is proposed for robot manipulator applications in this paper. The proposed control structure is a combination of a nonlinear PD control structure and a directly learning structure. Consequently, this new control method possesses both adaptive and on-line learning properties. One of the unique features of the NPD-LC algorithm is that the learning is based on the previous torque profile of the repetitive task. It is proved that the NPD-LC enjoys the asymptotic convergence for both tracking positions and tracking velocities. Simulation studies were conducted by comparing the proposed method with many other existing methods. As a result, it was demonstrated that the NPD-LC method can achieve a faster convergence speed. The proposed NPD-LC is robust and can be implemented for the control of robot manipulators.
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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