Algebraic Nonlinear Identification and Output Tracking Control of Synchronous Generator using Differential Flatness
Bibliographic record
Abstract
A kernel-based approach is explored to enhance robustness of flatness-based nonlinear tracking control design for a synchronous generator machine. The design involves full system identification and nonlinear filtering of the system state, to permit effective implementation of a nonlinear controller based on differential flatness of the model. The difficulty associated with robust implementations of flatness-based controllers resides in the necessity of fast and accurate estimation of higher order derivatives of the noisy, observed flat output. The recently developed forward-backward kernel estimation methods [1], lend themselves powerfully for this task. Two LTI surrogate models are used with the nonlinear model of the machine to serve identification and filtering of the state, and are switched seamlessly to generate persistent excitation for the purpose of a complex nonlinear identification of all the system parameters. The approach does not require a separate start-up phase for identification purposes. The need for on-line adaptive identification and associated re-tuning of the controller is detected and implemented during full operation of the machine. Neither the identification nor the state estimation procedures need any re-initialization while rendering improved accuracy of derivative estimates due to the forward-backward smoothing feature of the kernels involved.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".