{"title":"非线性倒立摆鲁棒控制的GWO新探索/开发","authors":"S. M. Djaouti, M. Khelfi, M. Malki, S. Mohammed","doi":"10.4114/intartif.vol26iss72pp30-43","DOIUrl":null,"url":null,"abstract":"Tuning a nonlinear inverted pendulum is a complex and uncertain optimization problem. In this paper, we develop two new GWO variants by introducing a DLH (Dimension Learning-based Hunting) module and new formulas to enhance the exploitation/exploration ratio aiming to avoid local minima. A statistical analysis is carried out to compare the two proposed approaches with five GWO variants. After that, they are used to tune a PID and FSMC controller. The obtained results are promising even when compared to other approaches","PeriodicalId":176050,"journal":{"name":"Inteligencia Artif.","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"New Exploration/Exploitation Improvements of GWO for Robust Control of a Nonlinear Inverted Pendulum\",\"authors\":\"S. M. Djaouti, M. Khelfi, M. Malki, S. Mohammed\",\"doi\":\"10.4114/intartif.vol26iss72pp30-43\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Tuning a nonlinear inverted pendulum is a complex and uncertain optimization problem. In this paper, we develop two new GWO variants by introducing a DLH (Dimension Learning-based Hunting) module and new formulas to enhance the exploitation/exploration ratio aiming to avoid local minima. A statistical analysis is carried out to compare the two proposed approaches with five GWO variants. After that, they are used to tune a PID and FSMC controller. The obtained results are promising even when compared to other approaches\",\"PeriodicalId\":176050,\"journal\":{\"name\":\"Inteligencia Artif.\",\"volume\":\"42 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-06-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Inteligencia Artif.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4114/intartif.vol26iss72pp30-43\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Inteligencia Artif.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4114/intartif.vol26iss72pp30-43","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
New Exploration/Exploitation Improvements of GWO for Robust Control of a Nonlinear Inverted Pendulum
Tuning a nonlinear inverted pendulum is a complex and uncertain optimization problem. In this paper, we develop two new GWO variants by introducing a DLH (Dimension Learning-based Hunting) module and new formulas to enhance the exploitation/exploration ratio aiming to avoid local minima. A statistical analysis is carried out to compare the two proposed approaches with five GWO variants. After that, they are used to tune a PID and FSMC controller. The obtained results are promising even when compared to other approaches