Position and Speed Control for Permanent Magnet DC Motor Based on Different Optimization Algorithms
Bibliographic record
Abstract
This paper focused on the position and speed control of Permanent Magnet DC (PMDC) motor based on Proportional-Integral-Derivative (PID) controller.The mathematical model of PMDC motor is presented with aid of Matlab /Simulink which shows the main equations of these motors.The optimum values of PID parameters are evaluated according to three different optimization algorithms: Ant Colony Optimization (ACO) which is based on cooperative behavior of real ant colonies, Grey Wolf Optimization (GWO) is a natureinspired meta-heuristic algorithm inspired by the social hierarchy and hunting behavior of grey wolves, and Flower Pollination Algorithm (FPA) is an algorithm inspired by the process of flower pollination.Position and speed of PMDC motor are investigated under different conditions of operation.According to the simulation results, employing optimization techniques improves the performance of conventional PID controllers to provide a better response for the motor.Finally, the results of the comparison in terms of speed and position of the motor show that the use of algorithms in the PID controller will improve the behavior (overshoot and settling time) of the motor significantly compared with the conventional PID controller at different loading conditions.The oscillations will be reduced effectively and clearly with optimum values of error.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".