{"title":"Comparison of PI and Super-twisting Controller Optimized with SCA and PSO for Speed Control of BLDC Motor","authors":"Özge Gülbaş, Yakup Hameş, Murat Furat","doi":"10.1109/HORA49412.2020.9152853","DOIUrl":null,"url":null,"abstract":"In the present study, sine-cosine and particle swarm optimization algorithms are used to determine optimal parameters of PI and super-twisting controllers for BLDC motor speed control. Common properties of optimization algorithms are kept same such as number of solution candidates, search space bounds, number of iterations and fitness functions. The fitness function is defined as a function of control effort, integral squared error and standard deviation of output and control effort. In the simulations, to avoid the overshoot at the output, a decay coefficient is introduced in the fitness function. The performance of the optimization algorithms is given with statistically and graphically.","PeriodicalId":166917,"journal":{"name":"2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HORA49412.2020.9152853","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
In the present study, sine-cosine and particle swarm optimization algorithms are used to determine optimal parameters of PI and super-twisting controllers for BLDC motor speed control. Common properties of optimization algorithms are kept same such as number of solution candidates, search space bounds, number of iterations and fitness functions. The fitness function is defined as a function of control effort, integral squared error and standard deviation of output and control effort. In the simulations, to avoid the overshoot at the output, a decay coefficient is introduced in the fitness function. The performance of the optimization algorithms is given with statistically and graphically.