CJEE

Chinese Journal of Electrical Engineering ›› 2020, Vol. 6 ›› Issue (3): 98-105.doi: 10.23919/CJEE.2020.000023

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New Optimization Design Method for a Double Secondary Linear Motor Based on R-DNN Modeling Method and MCS Optimization Algorithm*

Weitao Wang1, Jiwen Zhao2,*, Yang Zhou   

  1. 1. School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China;
    2. School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China
  • Received:2020-06-17 Revised:2020-07-14 Accepted:2020-07-21 Published:2020-10-14
  • Contact: * E-mail: 06009@ahu.edu.cn
  • About author:Weitao Wang was born in Fuyang, China. He received a B.S. degree in electrical engineering and automation from Anhui University, Hefei, China, in 2018. Since 2018, he has been working toward an M.S. degree in detection technology and automatic equipment at the School of Electrical Engineering, Anhui University, Hefei, China. His current research interests include the design, analysis, and optimization of linear motors.
    Jiwen Zhao was born in Dangshan, China. He received his Ph.D. degree in 2005 from the University of Science and Technology of China, Hefei, China. Since 2019, he has been working at the School of Electrical and Automation Engineering at Hefei University of Technology, Hefei, China. He has authored/coauthored more than 50 papers in international journals, such as IEEE Transactions on Industrial Electronics, IEEE Transactions on Industrial Informatics, and IEEE Transactions on Industrial Applications. He is a reviewer for more than ten SCI/SCIE journals. His research interests include linear motor optimization design, linear motor control, and photoelectric detection technology.
    Yang Zhou was born in Lu'an, China. He received a B.S. degree in electrical engineering and automation while minoring in software engineering in 2018 from Anhui University, Hefei, China. Since 2018, he has been working toward an M.S. degree in control engineering with the School of Electrical Engineering and Automation, Anhui University, Hefei, China. His current research interests include electrical machines, image processing, and photoelectric detection technology.
    Fei Dong was born in Yuncheng, China. She received a B.S. degree in mechanical engineering and an M.S. degree in mechatronic engineering from Chang'an University, Xi'an, China, in 2006 and 2011, respectively. Since 2011, she has been a lecturer with the School of Electrical Engineering and Automation, Anhui University, Hefei, China. Her research interests include the design, analysis, and optimization of permanent magnet synchronous motors.
  • Supported by:
    *National Natural Science Foundation of China (51837001, 51907001, 51707002).

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Abstract: Traditional linear motor optimization methods typically use analytical models combined with intelligent optimization algorithms. However, this approach has disadvantages, e.g., the analytical model might not be accurate enough, and the intelligent optimization algorithm can easily fall into local optimization. A new linear motor optimization strategy combining an R-deep neural network (R-DNN) and modified cuckoo search (MCS) is proposed; additionally, the thrust lifting and thrust fluctuation reductions are regarded as optimization objectives. The R-DNN is a deep neural network modeling method using the rectified linear unit (RELU) activation function, and the MCS provides a faster convergence speed and stronger data search capability as compared with genetic algorithms, particle swarm optimization, and standard CS algorithms. Finally, the validity and accuracy of this work are proven based on prototype experiments.

Key words: Double secondary linear motor (DSLM), machine learning modeling, R-deep neural network (R-DNN) algorithm, intelligent optimization algorithm, modified cuckoo search (MCS) algorithm