Design of a robust neuro-controller for complex dynamic systems
Bibliographic record
Abstract
Design of neuro-controller for complex dynamic systems is a big challenge faced by the researchers. In this paper we present a design of a robust neuro-controller for a dynamic system to make the system response fast with no overshoot. Here the control action decided by the controller completely depends on the value of the error at that point of time. The position feedback which controls the bandwidth of the system as well as the dynamic response is a function of the system error. For large error the position feedback is made large increasing the bandwidth of the system, and for small errors the position feedback value is small. Thus, during the dynamic response of the system the bandwidth of the system is controlled by the system error. Similarly, the velocity feedback which controls the damping in the system is kept very small for large errors, and large for small errors. Thus, in the proposed neuro-controller the position feedback K <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> (e,t), and velocity feedback K <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">v</sub> (e,t) are made as a function of error which yields a very fast response with no or very little overshoot.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".