Excitation Signal Design for Parameter Convergence in Adaptive Control of Linearizable Systems
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Bibliographic record
Abstract
In most adaptive control approaches, parameter convergence to their true values can only be ensured if the closed-loop trajectories provide sufficient excitation for the parameter estimation method. In this paper, the design of excitation signal for the adaptive control of linearizable systems is investigated. Based on a sufficient richness condition, two approaches for generating perturbation signals to achieve a desired level of excitation are presented. Moreover, since constant persistently exciting input may deteriorates control performance, we provide a formal design technique for adjusting the excitation magnitude on-line to meet the conflicting objectives of control and identification. The algorithm attenuates the PE signal as parameter convergence is achieved and re-activates it only when required. A simulation example is used to illustrate the developed procedure and ascertain our theoretical results
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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