Nonlinear Neural Control Strategies versus Conventional Control — Case Study and Performance Comparison
Why this work is in the frame
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Bibliographic record
Abstract
The main objective of this paper is to design and implement in MATLAB Simulink R2023b programming environment an intelligent nonlinear neural control strategy for a full-state feedback linearization nonlinear plant model which belongs to a particular class of second degree of linearization. The key idea consists of the use the input-output dataset measurements of a conventional proportional-integral-derivative controller connected in a closed loop control structure with the nonlinear plant. It works in real time based on the online acquisition of the input-output dataset that is processed by a combination of shallow or deep learning neural network structures for its mapping. For “proof concept” and simulation purposes, a model of a shunt-connected dc motor is under investigation as a case study. The effectiveness of the proposed algorithm is demonstrated through an intensive number of simulations conducted on MATLAB Simulink programming platform. For performance analysis comparison, a benchmark is constructed based on the statistic indicators calculated for three control strategies, very useful to reveal the improvements.
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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