Design an Optimal Fractional Order PID Controller for Speed Control of Electric Vehicle
Bibliographic record
Abstract
In recent years, electric vehicles have garnered significant attention due to their environmental and economic advantages compared to conventional vehicles, including reduced emissions and lower fuel costs.This study proposes an optimal fractional-order PID (FOPID) controller to regulate electric vehicle (EV) speed.The FOPID controller is advantageous due to its ability for stabilizing the system, managing parameter variations, and mitigating potential disturbances.The tuning of this controller's gains is achieved through an intelligent Ant Colony Optimization (ACO) algorithm.The selection of the gain values is strategically based on minimizing error, thereby ensuring a robust system response without overshoot or undershoots.The performance of the proposed controller is analyzed and compared to a classical PID controller for demonstrating its superior performance.Simulation results illustrate the efficiency of the proposed controller, which exhibits no fluctuation or oscillation in its response (zero overshoot) and fast settling and rise times of 0.0476 and 0.0297, respectively.By using the optimal gains determined by the smart ACO, the proposed controller achieves a satisfactory and robust system response in controlling EV speed.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".