Modern Control and Applications of Compound DC Motors: Performance, Techniques, and Developments
Bibliographic record
Abstract
In this study, a compound DC motor under different operating conditions is modeled, analyzed, and controlled.Preliminary open-loop simulations at various input voltages revealed limitations, such as slow rise time and steady-state error.Conventional controllers-proportional (P), proportional-integral (PI), and proportional-integralderivative (PID)-were assessed to control motor speed.A sensitivity analysis, varying key parameters (Ra, J, Ke, and Kt) by ±20%, confirmed the system's robustness and dependence on motor constants.Simulation results showed that the PI controller eliminates steady-state error and speeds up stabilization, the PID controller improves damping and reduces rise time and overshoot, and the P controller enhances transient performance while maintaining steady-state error.By optimizing the PID gains using a grid search approach, the advantages of modern control techniques are demonstrated.This method outperforms conventional PID by achieving faster start-up, less overshoot, and minimal steady-state error.The optimized PID, while maintaining the reference speed at 1500 rpm, provided the quickest start-up and stop times.Among all controllers tested, it delivered the best overall performance, highlighting the critical role of modern control strategies in enhancing DC motor performance and ensuring reliable operation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".