Fuzzy and optimal control of a two-link flexible manipulator
Why this work is in the frame
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Bibliographic record
Abstract
A linear quadratic Gaussian (LQG) strategy controls a two-link flexible robot manipulator tracking a two-dimensional square trajectory 12.6 m/spl times/12.6 m. Slew angles together with a Gaussian white process and white or non-white measurement noise are fed into a LQG regulator (Kalman filter). A FLC strategy incorporates two fuzzy controllers substituted for the LQR state-space dynamics equations. Trajectories were obtained for the LQG strategy with Gaussian white and non-white measurement noise. The trajectory obtained for white measurement noise closely approaches a perfect square while those obtained for non-white measurement noise deviate. The trajectory obtained with the FLC strategy is similar to that for LQG with white measurement noise. Fuzzy control is found to provide robustness in operation and can be constructed with less mathematical complexity than LQG. The deviation in trajectories for LQG with non-white measurement noise suggests sub-optimal control and possible instability. This study has demonstrated the extent of deviation in tracking with an LQG strategy for non-white measurement noise and the FLC strategy as a viable option for precise and robust tracking control of a two-link flexible robot manipulator.
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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