The Study of Permanent Magnetic Synchronous Motor Control System Through the Combination of BP Neural Network and PID Control

  • Lin Zhang
  • Bao-Jie Xu
  • Kun-Li Wen
  • Yuan-Hui Li
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 234)


Modern manufacturing is not only more demanding on machining accuracy but also requires the equipment to have a better degree of wisdom. For PMSM control system, it generally uses traditional PID control method due to the control advantages of traditional PID control which are simple algorithm, strong bond, and high reliability. However, the actual industrial processes are often nonlinear, and many nonlinear systems have difficulties to determine the precise mathematical model, which causes PID controller to not achieve ideal control effect. Because BP neural network has arbitrary nonlinear express ability which can achieve the best combination of PID control through the study of system performance. Hence, the control accuracy, robustness, and adaptive capacity of the control system for permanent magnet synchronous motors are improved. Also, PMSM vector control model is established to be a controlled subject. The chapter proposes the advantages of PID control and BP neural network to develop BP neural network PID controller. By using double-layer neural network controller with three inputs and three outputs, and the input refers to deviation, input signal and system output. After correcting the weightings and adjusting the three parameters of PID controller, the purpose of eliminating transient error rapidly and reaching steady state can be achieved. The practical simulation results find that the proposed BP neural network PID controller has parameter self-tuning function, short system response time, no over shooting phenomenon, and stronger robustness.


PMSM PID BP neural network Self-adjust Robustness 



The authors want to heartily thank Beijing Information Science & Technology University and Chienkuo Technology University for the financial supporting of this chapter.


  1. 1.
    Ang K, Chong G, Li Y (2005) PID control system analysis. Design and technology. IEEE Trans Control Syst Technol 13:559–576CrossRefGoogle Scholar
  2. 2.
    Shitao H, Zhijing F (2006) Design of optimal PID controller for liner servo system based on LQR approach. Manuf Technol Mach Tool:33–35Google Scholar
  3. 3.
    Jeng YF, Zhang L, Xu BJ, Wen KL (2012) The study of fuzzy PID controller in permanent magnetic synchronous motor. In: International conference on ICADE, pp 176–180Google Scholar
  4. 4.
    Hu HJ (2001) Stable and adaptive PID control based on neural network. J Beijing Univ Aeronaut Astronaut 27(2):153–156Google Scholar
  5. 5.
    Chen WB, Zeng GH, Zou HJ, Zhang HB, Tan CW (2012) Study of a single neuron fuzzy PID DC motor control method. In: International conference on intelligent systems design and engineering application, pp 1125–1128Google Scholar
  6. 6.
    Ablameyko S, Goras L, Gor M (2001) Neural networks for instrumentation. Measurement and related. Ios Press, AmsterdamGoogle Scholar
  7. 7.
    Chen YL, Chen WL et al (2005) Development of the FES system with neural network + PID controller for the stroke circuits and systems. In: International conference on IEEE ISCAS, Kobe, 2005, pp 5119–5121Google Scholar
  8. 8.
    Guo BT, Liu HY, Luo Z, Wang F (2009) Adaptive PID controller based on BP neural network. In: International joint conference on artificial intelligence, Pasadena, 2009, pp 148–150Google Scholar
  9. 9.
    Jang JSR (2008) Matlab program design. TeraSoft Inc, HsinchhuGoogle Scholar
  10. 10.
    Lee YD (2011) The design and simulation of control system: use Matlab/Simulink. CHWA, TaipeiGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Lin Zhang
    • 1
  • Bao-Jie Xu
    • 1
  • Kun-Li Wen
    • 2
  • Yuan-Hui Li
    • 3
  1. 1.Mechanical & Electrical Engineering SchoolBeijing Information Science & Technology UniversityBeijingChina
  2. 2.Department of Electrical EngineeringChienkuo Technology UniversityChanghuaTaiwan
  3. 3.Manufacturing Engineering DepartmentBeijing Foton Cummins CompanyBeijingChina

Personalised recommendations