Open AccessParameter Tuning of Brushless DC Motor for Improving Control Effect with Worm Algorithm
Author(s) -
Jian-Hua Qin,
Wenrong Wang,
Xiao Liu
Publication year - 2021
Publication title -
european journal of electrical engineering
Language(s) - English
Resource type - Journals
eISSN - 2116-7109
pISSN - 2103-3641
DOI - 10.18280/ejee.230307
Subject(s) - control theory (sociology) , particle swarm optimization , dc motor , controller (irrigation) , electronic speed control , genetic algorithm , computer science , phase (matter) , algorithm , mathematics , control (management) , engineering , physics , mathematical optimization , artificial intelligence , agronomy , quantum mechanics , electrical engineering , biology
Aiming at the problem of low control precision and small applicable scope caused by adjusting control parameters in Ziegler-Nichols (ZN) method, a parameter tuning method based on Worm algorithm (WOA) is proposed for Brushless DC motor. Firstly, the model of speed control is established by proportional integral method for Brushless DC motor with two - phase conduction and three - phase full bridge drive. Then the fitness function of the controller is constructed by the Integral Absolute Error (IAE). Finally, the early optimization process, the later movement rule and the peak extraction rule are determined for WOA, and the controller parameter tuning process is designed. Simulation results under constant and sinusoidal conditions show the effectiveness of the proposed method. WOA was compared with ZN, genetic algorithm (GA), differential evolution algorithm (DE) and particle swarm optimization algorithm (PSO) in the experiment. The experimental results show that the control effect (CE) of WOA under uniform speed has been improved by 2.56% on average, and has been improved by 16.93% on average under sinusoidal speed. Compared with previous methods, this method can be used for parameter adjustment of complex control with higher control precision.