Analytical Design of a Closed Control Loop Controller, Based on a Suboptimal Kalman-Busy Filter
Bibliographic record
Abstract
The Analytical Design (AD) of a closed, negative feedback control loop, when only single design criteria (potentially achievable accuracy) can be considered at the first stage of development for the synthesis of desired system dynamics. Such an approach, based on a modified suboptimal Kalman-Busy Filter (KBF) with Bounded Grows of Memory (FBGM), was presented in several previous author’s papers. In some cases, the required optimal controller should work, mainly, in the stationary stabilization mode in stationary conditions and, actually, is a regulator. In these cases, FBGM can be essentially simplified to a stationary Kalman’s state estimator, with a switched matrix weight coefficient (transient/stationary). The coefficient can, practically, be found rather from the conventional conditions for providing the system sufficient dynamics, than from the solution of KBF Riccati eq. A successful tuning makes the steady state accuracy be close to the optimal, provided by the KBF. The estimator is used for the estimation/filtering and control/regulation purposes simultaneously. This approach is considered in the below chapter to draw developer’s attention. A simple example of the 2nd order unit, assuming regulation of system angular position and angular velocity is presented.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".