Hybrid PD sliding mode control of a two degree-of-freedom parallel robotic manipulator
Bibliographic record
Abstract
One of the most important tasks in robotic applications is trajectory tracking. For such applications it is inherently important that the system obtains a high tracking performance. A widespread control method for trajectory tracking is PD control, which is well-known for its ease of implementation and acceptable tracking performance. However, for tasks that require high precision some advanced methods may be considered that provides higher tracking performance. One such a control method is sliding mode control (SMC), which provides robustness and low tracking errors. In this paper, using the hybridization concept, a new control law is formulated which combined the ease of implementation that PD control provides and the high tracking performance of SMC, while avoiding the inherent drawback of both. The so-called hybrid PD-SMC law provides model-free nonlinear feedback control. The effectiveness of the proposed hybrid control method is investigated through various simulation experiments that provide a comparison in both tracking performance and robustness with stand PD and SMC methods.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".