Optimal Tuning of PID-Controlled Magnetic Bearing System for Tracking Control of Pump Impeller in Artificial Heart
Bibliographic record
Abstract
In this work, using optimal PID control for magnetic bearing in artificial heart pump, two magnetic bearings used to suspend the impeller rotor, the small air gap, high speed of rotor that important think to keep the life of the human that uses Artificial Heart Ventricle, the Artificial Heart Ventricle it the is the full-actuated system the state-space model developed for the control, choosing the value of parameter control very important, the performance of output depending on this parameter. This study presents an optimization algorithm based on PSO (particle swarm optimization) to optimize performance of Proportional Integral Derivative (PID) controller to magnetically hanging the rotary pump impeller of Artificial Heart Ventricle (AHV). The optimal controller's terms are obtained by minimization of fitness function which is defined based on the index Root Mean Square of Error (RMSE). The optimal values of control elements lead to optimal PID controller which results in optimal tracking performance of PID controlled bearing system. The numerical simulation has been conducted to verify the effectiveness of proposed controller. The results showed that the optimal controller could stabilize the impeller within small deviations in displacement and angular position.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".