Performance and Robustness Enhancement of Fractional Order Controller (FOC) for Electric Vehicles (EV) Using Intelligent Swarms
Bibliographic record
Abstract
As the impacts of global warming intensify, the automotive industry is increasingly emphasizing the development of eco-friendly vehicles with superior range and performance compared to conventional ones.Electric vehicles have emerged as a promising solution to reduce harmful emissions in the transportation sector.This study focuses on creating a nonlinear dynamic model for electric vehicles by integrating kinetic and electrical components.The key criterion in EV speed control is robustness, prompting the construction of various controllers to ensure resilience and disturbance rejection.These controllers include both traditional ones like proportional-integral-derivative (PID) and fractional order PID (FOPID) controllers.Fractional calculus has gained significant attention in control systems engineering due to the fractional orders of the integral and derivative terms, offering enhanced robustness and optimal control.This thesis employs multiple optimization strategies to design an FOPID controller, ensuring the optimal performance of a robust control system for electric vehicles.Initially, the controller is developed using intelligent swarm optimization techniques, such as particle swarm optimization (PSO) and grey wolf optimization (GWO), through simulations in MATLAB R2022b.The results demonstrate the effectiveness of PSO and GWO algorithms in reducing the objective integral of time multiplied by absolute error (ITAE) function in the speed control system utilizing an FOPID controller.The performance of a conventional PID controller is compared with that of a FOPID controller, highlighting the superiority of the GWO-FOPID strategy, presented as a novel methodology.The outcomes underscore the remarkable performance of the GWO-FOPID controller, ensuring rapid responsiveness in controlling EV speed, with a rise time of 0.008978 seconds, a settling time of 0.01 seconds, and zero absolute time error (ITAE).
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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 | 0.000 | 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".