Username   Password       Forgot your password?  Forgot your username? 

ISSUES BY YEAR

Volume 14 - 2018

No.1 January 2018
No.1 January 2018
No.3 March 2018
No.3 March 2018
No.4 April 2018
No.4 April 2018
No.5 May 2018
No.5 May 2018

Volume 13 - 2017

No.4 July 2017
No.4 July 2017
No.5 September 2017
No.5 September 2017
No.7 November 2017
No.7 November 2017
No.8 December 2017
No.8 December 2017

Volume 12 - 2016

Volume 11 - 2015

Volume 10 - 2014

Volume 9 - 2013

Volume 8 - 2012

Volume 7 - 2011

Volume 6 - 2010

Volume 5 - 2009

Volume 4 - 2008

Volume 3 - 2007

Volume 2 - 2006

 

Brushless DC Motor Control Strategy for Electric Vehicles

Volume 14, Number 3, March 2018, pp. 559566
DOI: 10.23940/ijpe.18.03.p16.559566

Wanmin Lia, Xinyong Lib, Yan Wanga, Xianhao Zenga, and Yunzi Yanga

aSchool of Automobile Engineering, Lanzhou Institute of Technology, Lanzhou, 730050, China
bSchool of Mechanical Engineering, Changshu Institute of Technology, Suzhou, 215500, China

(Submitted on December 16, 2017; Revised on January 17, 2018; Accepted on February 20, 2018)


Abstract:

A self-adaptive fuzzy proportional integral derivative (PID) control method based on genetic optimization is proposed to solve the problem of low precision and low anti-jamming capabilities of the brushless direct current (DC) motor control system of electric vehicles. A double closed-loop speed control system model of the drive motor is established based on an analysis of the mathematic model of a permanent magnet brushless DC motor. Adaptive fuzzy PID control is introduced. The fuzzy membership function is optimized by the genetic algorithm and referred to as the optimized adaptive fuzzy PID control method. The design and simulation of the system are realized by using MATLAB/Simulink. Results show that in the same environment, the genetic algorithm with adaptive fuzzy PID control has better dynamic and static performance than ordinary and fuzzy PID. It has a good speed and anti-interference ability in a typical city driving environment.

 

References: 10

  1. A. Darba, F. D. Belie , P. D. Haese and J. A. Melkebeek, “Improved Dynamic Behavior in BLDC Drives Using Model Predictive Speed and Current Control,” IEEE Transactions on Industrial Electronics, vol. 63, no. 2, pp. 728–740, 2016
  2. A. A. Fahmy and A. M. A. Ghany, “Adaptive functional-based neuro-fuzzy PID incremental controller structure,” Neural Computing and Applications, vol. 26, no. 6, pp. 1423–1438, 2015
  3. J. Li and Y. Zhong, “Robust speed control of induction motor drives using first-order auto-disturbance rejection controllers,” IEEE Industry Applications Society Meeting, vol. 51, no. 1, pp. 712–720, 2015
  4. W. M. Li, L. M. Gu and L. L. Wei “Speed Control Simulation of the Electric Vehicle Driving Motor,” International Journal of Performability Engineering, vol. 13, no. 7, pp. 1140-1146, 2017
  5. C. Navaneethakkannan and M. Sudha, “Analysis and Implementation of ANFIS-based Rotor Position Controller for BLDC Motors,” Journal of Power Electronics, vol. 16, no. 2, pp. 564–571, 2016
  6. V. K. S. Patel and A. K. Pandey, “Modeling and Simulation of Brushless DC Motor Using PWM Control Technique,” Internation-al Journal of Engineering Research and Applications, vol. 3, no. 3, pp. 612-620, 2013
  7. A. Pandian and R. Dhanasekaran, “Hybrid Anti-Windup Fuzzy PI Controller Based Direct Torque Control of Three Phase Induction Motor,” Applied Mechanics and Materials, vol. 3230, no.573, pp. 155–160, 2014
  8. A. L. Saleh and A. A. Obed, “Speed Control of Brushless DC Motor based on Fractional Order PID Controller,” International Journal of Computer Applications, vol. 95, no. 4, pp. 1-6, 2014.
  9. T. Vijayakumar, S. Muthukrishnan and G. Murugananth, “Genetic Algorithm Based Speed Control of PMDC Motor Using Low Cost PIC 16F877A Microcontroller,” Circuits and Systems, vol. 7, no. 8, pp. 1334-1340, 2016.
  10. H. Yau, P. Yu and Y. h. Su, “Design and Implementation of Optimal Fuzzy PID Controller for DC Servo Motor,” Applied Mathematics & Information Sciences, vol. 8, pp. 231-237, 2014

 

Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader

Attachments:
Download this file (IJPE-2018-03-16.pdf)IJPE-2018-03-16.pdf[Brushless DC Motor Control Strategy for Electric Vehicles]685 Kb
 

CURRENT ISSUE

Prev Next

Temporal Multiscale Consumption Strategies of Intermittent Energy based on Parallel Computing

Huifen Chen, Yiming Zhang, Feng Yao, Zhice Yang, Fang Liu, Yi Liu, Zhiheng Li, and Jinggang Wang

Read more

Decision Tree Incremental Learning Algorithm Oriented Intelligence Data

Hongbin Wang, Ci Chu, Xiaodong Xie, Nianbin Wang, and Jing Sun

Read more

Spark-based Ensemble Learning for Imbalanced Data Classification

Jiaman Ding, Sichen Wang, Lianyin Jia, Jinguo You, and Ying Jiang

Read more

Classification Decision based on a Hybrid Method of Weighted kNN and Hyper-Sphere SVM

Peng Chen, Guoyou Shi, Shuang Liu, Yuanqiang Zhang, and Denis Špelič

Read more

An Improved Algorithm based on Time Domain Network Evolution

Guanghui Yan, Qingqing Ma, Yafei Wang, Yu Wu, and Dan Jin

Read more

Auto-Tuning for Solving Multi-Conditional MAD Model

Feng Yao, Yi Liu, Huifen Chen, Chen Li, Zhonghua Lu, Jinggang Wang, Zhiheng Li, and Ningming Nie

Read more

Smart Mine Construction based on Knowledge Engineering and Internet of Things

Xiaosan Ge, Shuai Su, Haiyang Yu, Gang Chen, and Xiaoping Lu

Read more

A Mining Model of Network Log Data based on Hadoop

Yun Wu, Xin Ma, Guangqian Kong, Bin Wang, and Xinwei Niu

Read more
This site uses encryption for transmitting your passwords. ratmilwebsolutions.com