Neurofuzzy control of modular and reconfigurable robots
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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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".