A self-tuned fuzzy-neural-network-based BLDCM speed control strategy

Shi, Tingna; Niu, Xingzhuo; Xiao, Zhenfeng; Li, X.M.; Xia, Chaoying

doi:10.1049/cp.2016.0334

Cited by 3 publications

(1 citation statement)

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“…However, the PID controller cannot yield a good control algorithm if the controlled object is highly nonlinear and uncertain [7]. Though there exists a model, BLDCM is also susceptible to un-modeled dynamics, parameter variations, uncertainties, and disturbances, such as the impact of electrical and mechanical disturbances in practical operation, which limits the application of control algorithms relying on the model [8].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fuzzy Neural Network PID Algorithm for BLDCM Speed Control System

Yin

Wang

et al. 2021

Mathematics

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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.

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Section: Introductionmentioning

confidence: 99%