An Adaptive Evolutionary Switching Control for Robot Manipulators
Bibliographic record
Abstract
In this paper, a new adaptive switching control approach, called adaptive evolutionary switching PD control (AES-PD), is proposed for iterative operations of robot manipulators. The proposed AES-PD control method is a combination of the feedback of PD control with gain switching and feedforward using the input torque profile obtained from the previous iteration. The asymptotic convergence of the AES-PD control method is theoretically proved using Lyapunov’s method. The philosophy of the switching control strategy is interpreted in the context of the iteration domain to increase the speed of the convergence for trajectory tracking of robot manipulators. The AES-PD control has a simple control structure that makes it easily implemented. The validity of the proposed control scheme is demonstrated for the trajectory tracking of robot manipulators through simulation studies. Simulation results show that the AES-PD control can improve the tracking performance with an increase of the iteration number. The EAS-PD control method has the adaptive and learning ability; therefore, it should be very attractive to applications of industrial robot control.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".