Speed Control of Motor Based on Improved Glowworm Swarm Optimization

作     者：Zhenzhou Wang Yan Zhang Pingping Yu Ning Cao Heiner Dintera 

作者机构：School of Information Science and EngineeringHebei University of Science and TechnologyShijiazhuang050018China School of Internet of Things and Software TechnologyWuxi Vocational College of Science and TechnologyWuxi214028China German-Russian Institute of Advanced TechnologiesKaran420126Russia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2021年第69卷第10期

页      面：503-519页

核心收录：

学科分类：0810[工学-信息与通信工程] 08[工学] 081001[工学-通信与信息系统] 

基　　金：This research was funded by the Hebei Science and Technology Support Program Project(19273703D) the Hebei Higher Education Science and Technology Research Project(ZD2020318) 

主　　题：PID speed control improved Glowworm Swarm Optimization brushless DC motor 

摘      要：To better regulate the speed of brushless DC motors,an improved algorithm based on the original Glowworm Swarm Optimization is *** proposed algorithm solves the problems of poor robustness,slow convergence,and low accuracy exhibited by traditional PID *** selecting the glowworm neighborhood set,an optimization scheme based on the growth and competition behavior of weeds is applied to a single glowworm to prevent falling into a local optimal *** the glowworm’s position is updated,the league selection operator is introduced to search for the global optimal *** the local search ability of the invasive weed optimization with the global search ability of the league selection operator enhances the robustness of the algorithm and also accelerates the convergence speed of the *** mathematical model of the brushless DC motor is established,the PID parameters are tuned and optimized using improved Glowworm Swarm Optimization algorithm,and the speed of the brushless DC motor is *** a Simulink environment,a double closed-loop speed control model was established to simulate the speed control of a brushless DC motor,and this simulation was compared with a traditional PID *** simulation results show that the model based on the improved Glowworm Swarm Optimization algorithm has good robustness and a steady-state response speed for motor speed control.