Stability control of a 6 DOF biped in the dual-support phase using fuzzy control
Why this work is in the frame
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Bibliographic record
Abstract
This work investigates the feasibility of using a fuzzy controller to stabilize a biped robot in the dual-support phase. Taking as inputs the errors of the angular position and angular velocity for each joint, the controller uses a rule-base implemented with five membership functions to compute the joint torques of a 6 DOF biped robot. The input gains were computed at run-time using the Hip and Ankle Strategy (HAS), which sets a gain value for each joint based on the relative position of the center of gravity with respect to the support area. Although the Hip and Ankle Strategy was found to increase the computational load without enhancing the actuation response, the results have clearly showed the success of the fuzzy control scheme in making the non-linear system stable, as well as in placing the robot into the most stable posture without having to lift the feet of the ground.
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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 | 0.001 | 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) | 0.001 | 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 it