A new control scheme for nonlinear systems with disturbances
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A new learning control scheme, based on a nonlinear disturbance observer (NDO) coupled with a sliding-mode fuzzy neural network (SFNN) with a feedback-error-learning (FEL) strategy, is proposed for a class of time-varying nonlinear systems with unknown disturbances. The proposed controller, referred to as NDOFEL, involves two steps for obtaining an estimate of the time-varying lumped disturbance d(t) for improving the precision of the tracking control. The NDO is initially applied to estimate d(t), but an observer error does not converge to zero since d/spl dot/(t)/spl ne/0. The SFNN is then presented to estimate the observer error such that the output of systems follows a desired trajectory. The proposed NDOFEL has stable on-line learning ability, maintains high control performance in the presence of disturbance, and guarantees the stability of closed-loop systems on the basis of the Lyapunov theorem. The effectiveness and robustness of the proposed NDOFEL is demonstrated through simulation results obtained for the tracking control during wing rock phenomena. The results suggest that the proposed controller can significantly enhance the tracking performance of aircraft.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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