Genetic Algorithm Turned PID Control of AFPMSM for Electrical Vehicle Application
| International Journal of Electrical and Electronics Engineering |
| © 2024 by SSRG - IJEEE Journal |
| Volume 11 Issue 2 |
| Year of Publication : 2024 |
| Authors : Nguyen Van Hai, Vo Thanh Ha |
10.14445/23488379/IJEEE-V11I2P113
How to Cite?
Nguyen Van Hai, Vo Thanh Ha, "Genetic Algorithm Turned PID Control of AFPMSM for Electrical Vehicle Application," SSRG International Journal of Electrical and Electronics Engineering, vol. 11, no. 2, pp. 119-128, 2024. Crossref, https://doi.org/10.14445/23488379/IJEEE-V11I2P113
Abstract:
This article describes the development of a torque controller for an Axial Flux Permanent Magnet Synchronous Motor (AFPMSM) using a Genetic Algorithm (GA) to optimize the two parameters of the PI controller. The GA aims to select the optimal set of two (Kp, Ki, Kd) for the PID controller, satisfying one of the objective functions IAE, ITAE, and MSE. After offline gene selection, regeneration, and mutation with upper and lower limit conditions, the two parameters of the PI controller are optimized to achieve the required torque and speed responses of the AFPMSM motor. The accuracy of the theory is validated through offline MATLAB/SIMULINK. The use of the genetic algorithm for optimizing the PID controller parameters has proven effective in achieving the desired torque and speed responses of the AFPMSM, demonstrating its potential for practical application in real-world scenarios. The optimized PID controller parameters can significantly improve the performance of the axial flux permanent magnet synchronous motor, leading to more precise torque and speed control. This approach offers a practical and efficient method for achieving desired motor responses, and it has the potential to be applied in various industrial and commercial applications. The use of genetic algorithms in motor control optimization demonstrates the continued advancements in control systems and their ability to meet the demands of real-world scenarios.
Keywords:
AFPMSM, GA, PID, Electrical Vehicle, QRF.
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