A neurodynamics based neuron-PID controller and its application to inverted pendulum
Bibliographic record
Abstract
A novel neuron-PID controller that has some excellent characteristics of nonlinear filtering and auto gain-regulation is developed for nonlinear systems in this paper. The biological neuron described by the shunting model is used to construct a nonlinear controller, which is based on the frame of a typical PID controller. The neural activity of the biological neuron model is stable, bounded and smooth so that the output of the neuron-PID controller is bounded and smooth. The proposed controller can be employed to design a class of flexible and safe control systems. The effectiveness and efficiency of the proposed control strategy have been demonstrated by applying it to the stabilization control of an inverted pendulum with uncertain dynamics. The simulations show that the dynamic responses of the control system can be effectively improved and the robustness of the proposed controller is better than that of the PID controller.
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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".