A Comparative Study of DC Motor Speed Control Techniques Using Fuzzy, SMC and PID
Bibliographic record
Abstract
An overview of intelligent control techniques for the speed control of a direct current (DC) motor has been described in this study.Using the MATLAB SIMULINK platform, the individually excited DC motor speed control system implemented as a physical model.A mathematical model for both the sliding mode control (SMC) method and PID control, a traditional control methodology that ensures the speed controller has been developed.For comparison's sake, fuzzy logic is constructed using the Mamdani Technique with two inputs to obtain the necessary speed control.In addition, to select the optimal gain, the output signal of The PID controller and SMC were contrasted with fuzzy logic in terms of overshoot peak and stability time period.The outcomes prove the SMC's superiority over the PI controller and fuzzy logic approach.Based on time domain characteristics, this article presents a comparative study between Proportional-Integral-Derivative (PID), a sliding mode control (SMC), and fuzzy logic controller (FLC) controllers.The study concludes the less overshoot peak and fast response through fixed Properties for the DC motor and its mechanical variations due to operating conditions.Based on transient response study, the results show that SMC is superior to fuzzy logic and classical controllers PID.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".