{"title":"基于改进萤火虫群优化算法的比例-积分-导数控制器参数优化","authors":"Xing Guo, Shi-Chao Yin, Yi Zhang, Wei Li","doi":"10.1504/ijcsm.2020.10028217","DOIUrl":null,"url":null,"abstract":"The proportional-integral-derivative (PID) controller parameters tuning, is seeking the optimal value in the space of three parameters to achieve the optimal control performance of the system. It is the core of contemporary feedback control system design. However, its easily falling into local optimum weakened its global search ability. To tackle this problem, this paper proposes an improved glowworm swarm optimisation algorithm, (D-AGSO) with the introduction of directed moving and adaptive step strategy. The simulation experimental results show that D-AGSO continuously adapts the tuning parameters, achieving lower fluctuations features, time settling and smaller steady state error, specially applied to the time delay in the case of inertia controlled system of industrial production.","PeriodicalId":399731,"journal":{"name":"Int. J. Comput. Sci. Math.","volume":"65 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Proportional-integral-derivative controller parameter optimisation based on improved glowworm swarm optimisation algorithm\",\"authors\":\"Xing Guo, Shi-Chao Yin, Yi Zhang, Wei Li\",\"doi\":\"10.1504/ijcsm.2020.10028217\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The proportional-integral-derivative (PID) controller parameters tuning, is seeking the optimal value in the space of three parameters to achieve the optimal control performance of the system. It is the core of contemporary feedback control system design. However, its easily falling into local optimum weakened its global search ability. To tackle this problem, this paper proposes an improved glowworm swarm optimisation algorithm, (D-AGSO) with the introduction of directed moving and adaptive step strategy. The simulation experimental results show that D-AGSO continuously adapts the tuning parameters, achieving lower fluctuations features, time settling and smaller steady state error, specially applied to the time delay in the case of inertia controlled system of industrial production.\",\"PeriodicalId\":399731,\"journal\":{\"name\":\"Int. J. Comput. Sci. Math.\",\"volume\":\"65 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-04-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Int. J. Comput. Sci. Math.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1504/ijcsm.2020.10028217\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. Comput. Sci. Math.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1504/ijcsm.2020.10028217","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Proportional-integral-derivative controller parameter optimisation based on improved glowworm swarm optimisation algorithm
The proportional-integral-derivative (PID) controller parameters tuning, is seeking the optimal value in the space of three parameters to achieve the optimal control performance of the system. It is the core of contemporary feedback control system design. However, its easily falling into local optimum weakened its global search ability. To tackle this problem, this paper proposes an improved glowworm swarm optimisation algorithm, (D-AGSO) with the introduction of directed moving and adaptive step strategy. The simulation experimental results show that D-AGSO continuously adapts the tuning parameters, achieving lower fluctuations features, time settling and smaller steady state error, specially applied to the time delay in the case of inertia controlled system of industrial production.