A Comparative Study of Performances Between the Sliding Modes and the Trust Control Strategies for an Articulated Robotic Arm Position Control
Bibliographic record
Abstract
Two robust control techniques are presented and applied to a SCARA robotic arm in order to analyze the performances in terms of precision and quality of the control signals. The first controller is the very well known sliding modes (SM) based controller. It has been applied successfully so far in various applications. Its main drawbacks is certainly the Shattering phenomenon and the high energy of the control signals deployed to overcome the modelling and measurement errors. The second controller is a brand new controller called the trust control. The trust controller is based on the Dempster-Shafer belief theory concepts. Its robustness with regard to the measurement and modelling errors is mainly due to the use of the trust allocated to the information sources (measurements and estimations) rather than the direct use of the data itself (possibly incorrect) in the control strategy computation. Simulations have been carried out, they underline the robustness of the trust controller and its higher precision and less energetic control signals, with comparison to the SM controller.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".