Self Learning Fuzzy Logic-Based Robust Control of Robotic Manipulators Driven With BLDC Motors: A Task Space Control Approach
Bibliographic record
Abstract
The primary objective of this study is to enable the end effector of robot manipulators driven by brushless DC motors (BLDC), subjected to model uncertainties, to track the desired trajectory. Direct control in task space, with the primary goal of minimizing the tracking error of the end effector, is favored. Besides, incorporating actuator dynamics (AD)actuator dynamics (AD) into control synthesis and stability analysis is intended to enhance the sensitivity in terms of positioning and the reliability of robot manipulators. Consideration is given to uncertainties in both the robot manipulator and AD to achieve enhanced tracking performance. In order to improve the efficiency of the closed-loop control system, uncertainties in the dynamic model and AD were estimated using a self-organized adaptive fuzzy logic (AFL)adaptive fuzzy logic (AFL) framework, and the obtained estimates were applied to the control torque input. In the employed AFL framework, the means and variances of the membership functions (MFs)membership functions (MFs) are updated online in each iteration, enabling a more accurate estimation of uncertainties. The use of the newly created Lyapunov function demonstrates that the closed-loop system is uniformly ultimately bound. Experimental comparisons were conducted on a two-degree-of-freedom planar robot manipulator driven by a BLDC motor to test the applicability of the presented 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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".