3-DOF-PID nonlinear ℋ<sub>∞</sub> estimators
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
Abstract In this article, we present the one-degree-of-freedom (1-DOF) or proportional (P), the 2-DOF or proportional-integral (PI) and the 3-DOF or proportional-integral-derivative (PID) ℋ∞ nonlinear filters as counterparts of the popular P, PI and PID controllers, respectively. We use the idea of certainty-equivalence principle in which the worst-case disturbance or noise is determined as a function of the estimated state and under the assumption that this converges to the actual value asymptotically. Sufficient conditions for the solvability of the various filter gains are derived in terms of new Hamilton–Jacobi–Isaacs equations, which do not involve the system states. Simulation results are also presented to support the theory. Keywords: nonlinear systemnonlinear filteringℒ2-gainPID-filtersHamilton–Jacobi–Isaacs equations Notes Note 1. For a manifold M, TM is the tangent bundle of M and T ⋆ M is the cotangent bundle of M.
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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