Parallel Flower Pollination Algorithm and Its Application to Fractional-Order PID Controller Design Optimization for BLDC Motor Speed Control System

Khluabwannarat, Prapapan

doi:10.15199/48.2020.11.16

Cited by 2 publications

(1 citation statement)

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“…In the CS algorithm, the nest is a single solution to the problem being analyzed. In each iteration of the algorithm, 25% of nests change their location, i.e., the p a probability is 0.25 [26,27]. The cuckoo starts from the i-th nest and lays an egg in the new nest, while N denotes the total number of cuckoos, and n is the nests numbers in the optimization algorithm.…”

Section: Cuckoo Search Algorithmmentioning

confidence: 99%

Minimization of Torque Ripple in the Brushless DC Motor Using Constrained Cuckoo Search Algorithm

2021

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This paper presents the application of the cuckoo search (CS) algorithm in attempts to the minimization of the commutation torque ripple in the brushless DC motor (BLDC). The optimization algorithm was created based on the cuckoo’s reproductive behavior. The lumped-parameters mathematical model of the BLDC motor was developed. The values of self-inductances, mutual inductances, and back-electromotive force waveforms applied in the mathematical model were calculated by the use of the finite element method. The optimization algorithm was developed in Python 3.8. The CS algorithm was coupled with the static penalty function. During the optimization process, the shape of the voltage supplying the stator windings was determined to minimize the commutation torque ripple. Selected results of computer simulation are presented and discussed.

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Section: Cuckoo Search Algorithmmentioning

confidence: 99%