Adaptive Fuzzy Neural Network PID Algorithm for BLDCM Speed Control System

Hongqiao Yin, Wenjun Yi, Jintao Wu, Kangjian Wang and Jun Guan
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Hongqiao Yin: National Key Laboratory of Transient Physics, Nanjing University of Science and Technology, Nanjing 210094, China
Wenjun Yi: National Key Laboratory of Transient Physics, Nanjing University of Science and Technology, Nanjing 210094, China
Jintao Wu: Beijing Institute of Astronautical System Engineering, China Academy of Launch Vehicle Technology, Beijing 100076, China
Kangjian Wang: National Key Laboratory of Transient Physics, Nanjing University of Science and Technology, Nanjing 210094, China
Jun Guan: National Key Laboratory of Transient Physics, Nanjing University of Science and Technology, Nanjing 210094, China

Mathematics, 2021, vol. 10, issue 1, 1-19

Abstract: Because of its simple structure, high efficiency, low noise, and high reliability, the brushless direct current motor (BLDCM) has an irreplaceable role compared with other types of motors in many aspects. The traditional proportional integral derivative (PID) control algorithm has been widely used in practical engineering because of its simple structure and convenient adjustment, but it has many shortcomings in control accuracy and other aspects. Therefore, in this paper, a fuzzy single neuron neural network (FSNNN) PID algorithm based on an automatic speed regulator (ASR) is designed and applied to a BLDCM control system. This paper introduces a BLDCM mathematical model and its control system and designs an FSNNN PID algorithm that takes speed deviation e at different sampling times as inputs of a neural network to adjust the PID parameters, and then it uses a fuzzy system to adjust gain K of the neural network. In addition, the frequency domain stability of a double closed loop PID control system is analyzed, and the control effect of traditional PID, fuzzy PID, and FSNNN PID algorithms are compared by setting different reference speeds, as well as the change rules of three-phase current, back electromotive force (EMF), electromagnetic torque, and rotor angle position. Finally, results show that a motor controlled by the FSNNN PID algorithm has certain superiority compared with traditional PID and fuzzy PID algorithms and also has better control effects.

Keywords: brushless direct current motor (BLDCM); automatic speed regulator (ASR); fuzzy single neuron neural network (FSNNN) algorithm; performance index (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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