Simulation and Experimental Evaluation of DC Motor Control Strategies Using MATLAB and Arduino Mega
Bibliographic record
Abstract
The paper presents a comprehensive analysis of three advanced control strategies: Proportional-Integral-Derivative (PID) controllers, Fuzzy Logic Controllers (FLC), and Sliding Mode Controllers (SMC) to achieve accurate speed control of a DC motor.The proposed study is conducted both theoretically and practically, utilizing MATLAB and AVR microcontrollers for real-time experiments.A modified SMC control law is introduced to enhance system performance, reduce the inherent chattering effect, and maintain robustness against parameter variations.The performance of each control strategy is evaluated based on key specifications, including system stability, response time, and adaptability to external disturbances.The findings highlight the strengths and limitations of each control approach and provide valuable insights for selecting the most suitable controller for specific applications.Additionally, the paper explores the integration of artificial intelligence techniques to optimize controller performance in dynamic and uncertain environments, contributing to the advancement of intelligent control systems.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 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".