{"title":"Whale Optimization Algorithm(WOA) Based Speed Control of BLDC Motor","authors":"Shubham Banerjee, Sarode Shiva Kumar, A. Alam","doi":"10.1109/SeFeT55524.2022.9909419","DOIUrl":null,"url":null,"abstract":"This paper aims at a comparative analysis between the implementation of a Proportional Integral(PI) controller using PSO(Particle Swarm Optimization) and WOA(Whale Optimization Algorithm) for speed regulation of a BLDC motor. The goal is to use the system mathematical model to lower the overall transient time and achieve the desired speed at the earliest for the closed-loop control of the machine. The BLDC motor modelled in Simulink was run using both the tuning algorithms, developed through MATLAB codes. Upon detailed comparison, it was observed that the WOA optimized PI control was successful in determining intricate gains (kp and ki values) within much lesser iterations and run-time than that of the control developed through Particle Swarm Optimization algorithm.","PeriodicalId":262863,"journal":{"name":"2022 IEEE 2nd International Conference on Sustainable Energy and Future Electric Transportation (SeFeT)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE 2nd International Conference on Sustainable Energy and Future Electric Transportation (SeFeT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SeFeT55524.2022.9909419","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This paper aims at a comparative analysis between the implementation of a Proportional Integral(PI) controller using PSO(Particle Swarm Optimization) and WOA(Whale Optimization Algorithm) for speed regulation of a BLDC motor. The goal is to use the system mathematical model to lower the overall transient time and achieve the desired speed at the earliest for the closed-loop control of the machine. The BLDC motor modelled in Simulink was run using both the tuning algorithms, developed through MATLAB codes. Upon detailed comparison, it was observed that the WOA optimized PI control was successful in determining intricate gains (kp and ki values) within much lesser iterations and run-time than that of the control developed through Particle Swarm Optimization algorithm.