A single-neuron PID adaptive multicontroller scheme based on RBFNN
Bibliographic record
Abstract
In order to improve the control performance of the multicontroller proposed by Guo and Jutan (Canadian Journal of Chemical Engineering, 79, 817-22, 2001), a single-neuron PID multicontroller scheme based on a radial base function neural network (RBFNN) is proposed in this paper. This scheme has four controllers, specifically a set-point controller, two load controllers and a proportional controller. These controllers may be designed independently to achieve good control performance for both set-point tracking and load rejection. In particular, the set-point controller and the load controller have been chosen as single-neuron PID controllers. The model parameters and the parameters of the two single-neuron PID controller are updated in real time. For simplicity, the feedforward controller can be chosen as a unity gain proportional controller. It guarantees physical realizability and provides complete compensation for measurable disturbance. The simulation results show that the single-neuron PID adaptive multicontroller scheme based on RBFNN is very effective and the controller is of relatively strong robustness.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".