Regular and fuzzy extended Kalman filtering for a two-link flexible robot manipulator
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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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 | 0.001 | 0.002 |
| 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 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".