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.
Highlights
The brushless direct current motor (BLDCM), called a permanent magnet synchronous motor, is one of the motor types that is coming of age due to continuous improvement in high energy permanent magnet materials, power semiconductors, digital integrated circuits, and computer technology
We proposed a fuzzy PI algorithm for BLDCM control systems, and results verified the superiority of the fuzzy PI algorithm for BLDCM double closed loop systems compared with traditional proportional integral derivative (PID) controls [42]
The transfer function of the motor is significant for performance analysis and control system design; the mathematical model of BLDCM in differential form expressed as the transfer function after Laplace transform is KT Ud (s)
Summary
The brushless direct current motor (BLDCM), called a permanent magnet synchronous motor, is one of the motor types that is coming of age due to continuous improvement in high energy permanent magnet materials, power semiconductors, digital integrated circuits, and computer technology. The fuzzy neural network PID algorithm makes use of advantages of both the learning capability of the neural network and the robustness of fuzzy logic control that compensates the disadvantages of modification of fuzzy rules or models. This paper presents a FSNNN PID algorithm, which makes full use of the advantages of strong adaptive ability of fuzzy control, and the strong self-learning, self-organization, and parallel computing of neural networks.
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