{"title":"基于现代优化技术的直流电机速度控制 PID 控制器调试","authors":"Subrata Pandey","doi":"10.36647/ciml/04.02.a004","DOIUrl":null,"url":null,"abstract":"In this paper, the optimal configuration of the Proportional Integral Derivative (PID) controller for the speed control of a DC motor are determined and compared using five optimization algorithms. The five optimization algorithms are respectively Ant Lion Optimization (ALO), Grey Wolf Optimizer (GWO), Moth-Flame Optimization (MFO), Multi-Verse Optimization (MVO) and Salp Swarm Optimization (SSO). The objective function uses The Integral of Time multiplied by Absolute Error (ITAE) as the performance index. A comparison of all these methods is done using the following step response parameters - steady-state error, settling time, maximum overshoot and rise time. ALO performed best among all the optimization algorithms.","PeriodicalId":203221,"journal":{"name":"Computational Intelligence and Machine Learning","volume":"8 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Modern Optimization Techniques Based PID Controller Tuning for the Speed Control of a DC Motor\",\"authors\":\"Subrata Pandey\",\"doi\":\"10.36647/ciml/04.02.a004\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, the optimal configuration of the Proportional Integral Derivative (PID) controller for the speed control of a DC motor are determined and compared using five optimization algorithms. The five optimization algorithms are respectively Ant Lion Optimization (ALO), Grey Wolf Optimizer (GWO), Moth-Flame Optimization (MFO), Multi-Verse Optimization (MVO) and Salp Swarm Optimization (SSO). The objective function uses The Integral of Time multiplied by Absolute Error (ITAE) as the performance index. A comparison of all these methods is done using the following step response parameters - steady-state error, settling time, maximum overshoot and rise time. ALO performed best among all the optimization algorithms.\",\"PeriodicalId\":203221,\"journal\":{\"name\":\"Computational Intelligence and Machine Learning\",\"volume\":\"8 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-10-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computational Intelligence and Machine Learning\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.36647/ciml/04.02.a004\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Intelligence and Machine Learning","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.36647/ciml/04.02.a004","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Modern Optimization Techniques Based PID Controller Tuning for the Speed Control of a DC Motor
In this paper, the optimal configuration of the Proportional Integral Derivative (PID) controller for the speed control of a DC motor are determined and compared using five optimization algorithms. The five optimization algorithms are respectively Ant Lion Optimization (ALO), Grey Wolf Optimizer (GWO), Moth-Flame Optimization (MFO), Multi-Verse Optimization (MVO) and Salp Swarm Optimization (SSO). The objective function uses The Integral of Time multiplied by Absolute Error (ITAE) as the performance index. A comparison of all these methods is done using the following step response parameters - steady-state error, settling time, maximum overshoot and rise time. ALO performed best among all the optimization algorithms.