Fuzzy and optimal control of a two-link flexible manipulator
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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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 | 0.001 | 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".