Robust Adaptive Fuzzy Output Feedback Control System for Robot Manipulators
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we propose a novel hybrid control system for the trajectory tracking control problem of robotic systems. The design combines fuzzy system with robust adaptive control algorithm. The fuzzy system approximates the certainty equivalent-based optimal controller, while a robustifying adaptive control term is used to cope with uncertainties due to the presence of the external disturbance, fuzzy approximation errors, and other modeling errors. Using the Lyapunov method, we first develop a stable hybrid controller by assuming that the system output and its derivatives are available for feedback control design. Then, an output-feedback form of the position-velocity (state-feedback) controller is proposed, where the unknown velocity signal is replaced by the output of a model-free linear estimator. We show that the tracking of the output-feedback design can converge asymptotically to the performance achieved under the state-feedback control design. Finally, the proposed method is evaluated on a robotic system to demonstrate the theoretical development.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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