{"title":"基于量子遗传算法的 T-S 模糊系统记忆状态反馈控制","authors":"K. Sanjay, R. Vijay Aravind, P. Balasubramaniam","doi":"10.1140/epjs/s11734-024-01293-1","DOIUrl":null,"url":null,"abstract":"<p>In this paper, the authors utilize a linear matrix inequality (LMI) technique for designing a quantum genetic algorithm (QGA)-based memory state feedback control of a nonlinear system. The performance of the proposed model is enhanced using the QGA-based algorithm for finding the control gain matrices as a searching tool. To evaluate the fitness function of QGA, the LMI problem is formulated as a constrained optimization. The more general Lyapunov–Krasovskii (LKFs) functional is selected to analyze the closed-loop system stability and the criterion for its asymptotic stability. Numerical examples are provided to verify the effectiveness of the QGA-based proposed control scheme.</p>","PeriodicalId":501403,"journal":{"name":"The European Physical Journal Special Topics","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Quantum genetic algorithm-based memory state feedback control for T–S fuzzy system\",\"authors\":\"K. Sanjay, R. Vijay Aravind, P. Balasubramaniam\",\"doi\":\"10.1140/epjs/s11734-024-01293-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>In this paper, the authors utilize a linear matrix inequality (LMI) technique for designing a quantum genetic algorithm (QGA)-based memory state feedback control of a nonlinear system. The performance of the proposed model is enhanced using the QGA-based algorithm for finding the control gain matrices as a searching tool. To evaluate the fitness function of QGA, the LMI problem is formulated as a constrained optimization. The more general Lyapunov–Krasovskii (LKFs) functional is selected to analyze the closed-loop system stability and the criterion for its asymptotic stability. Numerical examples are provided to verify the effectiveness of the QGA-based proposed control scheme.</p>\",\"PeriodicalId\":501403,\"journal\":{\"name\":\"The European Physical Journal Special Topics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-08-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The European Physical Journal Special Topics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1140/epjs/s11734-024-01293-1\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The European Physical Journal Special Topics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1140/epjs/s11734-024-01293-1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Quantum genetic algorithm-based memory state feedback control for T–S fuzzy system
In this paper, the authors utilize a linear matrix inequality (LMI) technique for designing a quantum genetic algorithm (QGA)-based memory state feedback control of a nonlinear system. The performance of the proposed model is enhanced using the QGA-based algorithm for finding the control gain matrices as a searching tool. To evaluate the fitness function of QGA, the LMI problem is formulated as a constrained optimization. The more general Lyapunov–Krasovskii (LKFs) functional is selected to analyze the closed-loop system stability and the criterion for its asymptotic stability. Numerical examples are provided to verify the effectiveness of the QGA-based proposed control scheme.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
