{"title":"基于改进灰狼优化器的分数阶PI λD μ控制器用于AVR系统","authors":"Santosh Kumar, R. Devarapalli","doi":"10.24425/acs.2022.141719","DOIUrl":null,"url":null,"abstract":"In this paper, an automatic voltage regulator (AVR) embedded with fractional order PID (FOPID) is employed for the alternator terminal voltage control. A novel meta-heuristic technique, a modified version of grey wolf optimizer (mGWO) is proposed to design and optimize the FOPID AVR system. The parameters of FOPID, namely, proportional gain ( 𝐾 𝑃 ) , the integral gain ( 𝐾 𝐼 ) , the derivative gain ( 𝐾 𝐷 ) , 𝜆 and 𝜇 have been optimally tuned with the proposed mGWO technique using a novel fitness function. The initial values of the 𝐾 𝑃 , 𝐾 𝐼 , and 𝐾 𝐷 of the FOPID controller are obtained using Ziegler-Nichols (ZN) method, whereas the initial values of 𝜆 and 𝜇 have been chosen as arbitrary values.The proposed algorithm offers more benefits such as easy implementation, fast convergence characteristics, and excellent computational ability for the optimization of functions with more than three variables. Additionally, the hasty tuning of FOPID controller parameters gives a high-quality result, and the proposed controller also improves the robustness of the system during uncertainties in the parameters. The quality of the simulated result of the proposed controller has been validatedby other state-of-the-art techniques in the literature.","PeriodicalId":48654,"journal":{"name":"Archives of Control Sciences","volume":"69 1","pages":""},"PeriodicalIF":1.2000,"publicationDate":"2023-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Fractional order PI λD μ controller with optimal parameters using Modified Grey Wolf Optimizer for AVR system\",\"authors\":\"Santosh Kumar, R. Devarapalli\",\"doi\":\"10.24425/acs.2022.141719\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, an automatic voltage regulator (AVR) embedded with fractional order PID (FOPID) is employed for the alternator terminal voltage control. A novel meta-heuristic technique, a modified version of grey wolf optimizer (mGWO) is proposed to design and optimize the FOPID AVR system. The parameters of FOPID, namely, proportional gain ( 𝐾 𝑃 ) , the integral gain ( 𝐾 𝐼 ) , the derivative gain ( 𝐾 𝐷 ) , 𝜆 and 𝜇 have been optimally tuned with the proposed mGWO technique using a novel fitness function. The initial values of the 𝐾 𝑃 , 𝐾 𝐼 , and 𝐾 𝐷 of the FOPID controller are obtained using Ziegler-Nichols (ZN) method, whereas the initial values of 𝜆 and 𝜇 have been chosen as arbitrary values.The proposed algorithm offers more benefits such as easy implementation, fast convergence characteristics, and excellent computational ability for the optimization of functions with more than three variables. Additionally, the hasty tuning of FOPID controller parameters gives a high-quality result, and the proposed controller also improves the robustness of the system during uncertainties in the parameters. The quality of the simulated result of the proposed controller has been validatedby other state-of-the-art techniques in the literature.\",\"PeriodicalId\":48654,\"journal\":{\"name\":\"Archives of Control Sciences\",\"volume\":\"69 1\",\"pages\":\"\"},\"PeriodicalIF\":1.2000,\"publicationDate\":\"2023-07-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Archives of Control Sciences\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.24425/acs.2022.141719\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Archives of Control Sciences","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.24425/acs.2022.141719","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
Fractional order PI λD μ controller with optimal parameters using Modified Grey Wolf Optimizer for AVR system
In this paper, an automatic voltage regulator (AVR) embedded with fractional order PID (FOPID) is employed for the alternator terminal voltage control. A novel meta-heuristic technique, a modified version of grey wolf optimizer (mGWO) is proposed to design and optimize the FOPID AVR system. The parameters of FOPID, namely, proportional gain ( 𝐾 𝑃 ) , the integral gain ( 𝐾 𝐼 ) , the derivative gain ( 𝐾 𝐷 ) , 𝜆 and 𝜇 have been optimally tuned with the proposed mGWO technique using a novel fitness function. The initial values of the 𝐾 𝑃 , 𝐾 𝐼 , and 𝐾 𝐷 of the FOPID controller are obtained using Ziegler-Nichols (ZN) method, whereas the initial values of 𝜆 and 𝜇 have been chosen as arbitrary values.The proposed algorithm offers more benefits such as easy implementation, fast convergence characteristics, and excellent computational ability for the optimization of functions with more than three variables. Additionally, the hasty tuning of FOPID controller parameters gives a high-quality result, and the proposed controller also improves the robustness of the system during uncertainties in the parameters. The quality of the simulated result of the proposed controller has been validatedby other state-of-the-art techniques in the literature.
期刊介绍:
Archives of Control Sciences welcomes for consideration papers on topics of significance in broadly understood control science and related areas, including: basic control theory, optimal control, optimization methods, control of complex systems, mathematical modeling of dynamic and control systems, expert and decision support systems and diverse methods of knowledge modelling and representing uncertainty (by stochastic, set-valued, fuzzy or rough set methods, etc.), robotics and flexible manufacturing systems. Related areas that are covered include information technology, parallel and distributed computations, neural networks and mathematical biomedicine, mathematical economics, applied game theory, financial engineering, business informatics and other similar fields.