Speed Regulation using MRAC based FOPID Controller In Current-Controlled DC Motor based on Integral Time Absolute Error Reduction
Bibliographic record
Abstract
The integration of renewable energy sources in industrial systems has led to a rising demand for DC motor drives, primarily due to their precise speed control capabilities. Although DC motors typically require more maintenance than their AC counterparts, their responsiveness makes them suitable for dynamic control applications. This study explores a speed regulation method for a current-controlled DC motor using a Fractional-Order PID (FOPID) controller, with parameter optimization guided by the Model Reference Adaptive Control (MRAC) approach based on the MIT rule. The motor system is modeled mathematically and implemented in MATLAB/Simulink to simulate both standard FOPID) and MRAC-tuned FOPID control strategies. The controller parameters are adaptively adjusted to minimize a defined performance criterion. The performance of both controllers is analyzed using key time-domain metrics—such as rise time, settling time, overshoot, and steady-state error. Simulation results confirm that the MRAC-based FOPID controller delivers superior dynamic performance, highlighting its potential for robust and efficient speed control in adaptive DC motor drive systems.
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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 | 0.000 | 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 | 0.001 | 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".