Regular and fuzzy extended Kalman filtering for a two-link flexible robot manipulator
Why this work is in the frame
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Bibliographic record
Abstract
A Linear quadratic Gaussian (LQG) control scheme with either a regular extended Kalman filter (EKF) or a fuzzy logic adaptive EKF (FLAEKF) state estimator implemented in the control loop was used to control a two-link flexible robot manipulator tracking a square trajectory 12.6m x 12.6m. Simulations were performed to ascertain the extent of divergence that may develop in a regular EKF and how effectively a FLAEKF could reduce or eliminate this divergence. Trajectories were obtained using LQG with a regular EKF resulting in divergence according to the intensity of non-white process and measurement noise disturbances. They were compared to more precise trajectories obtained using LQG with a FLAEKF. The results confirm the ability of a FLAEKF state estimator to effectively correct divergence that would otherwise occur with a regular EKF state estimator and to maintain robot-tracking precision albeit at a greater computational time burden.
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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