Stable adaptive output feedback controller for a class of uncertain non‐linear systems
Why this work is in the frame
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Bibliographic record
Abstract
This study deals with design of an adaptive output feedback tracking controller for a class of non‐linear systems with unknown fixed control direction. By using neural networks and deriving adaptive rules based on the steepest descent algorithm, the authors present a stable output feedback control scheme, which is applicable to a wide class of unknown complicated non‐linear systems. Therefore an approach based on the dynamic back propagation algorithm is proposed to develop the adaption laws for systems with more general model structure. Using Lyapunov's direct method, uniformly ultimately boundedness of all signals of the closed‐loop system is also ensured. Moreover, it is shown that the bounds on the tracking errors depend on the designing parameters. Hence, an arbitrarily small tracking error can be achieved by adjusting the parameters properly. Finally, simulation results performed on a non‐affine uncertain non‐linear system having internal dynamics are given to demonstrate the effectiveness of the proposed scheme and the theoretical discussions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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