Neurofuzzy control of modular and reconfigurable robots
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In recent years, the concept of modular and reconfigurable robotics emerged as a means for flexible and versatile automation. This concept allows for the execution of many complex tasks that cannot be performed by fixed-configuration manipulators. Nevertheless, reconfigurable robots introduce a challenging level of complexity to the problem of design of controllers that can handle a wide range of robot configurations with reliable performance. This paper addresses the position control of modular and reconfigurable robots. We develop a practical intelligent-control architecture that can be easily used in the presence of dynamic parameter uncertainty and unmodeled disturbances. The architecture requires no a priori knowledge of the system-dynamics parameters. Adaptive control is provided using fuzzy gain tuning of proportional-integral-derivative parameters in the presence of external disturbances. The architecture also provides learning control using feedforward neural networks. Moreover, the architecture has the capability of updating the adaptive control under reconfigurability. Experiments on a modular robot test bed are reported to validate the effectiveness of the control methodology.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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