{"title":"An improved swarm intelligence algorithm for multirate systems state estimation using the canonical state space models","authors":"Lin Lin, Weixing Lin, Xuhua Shi, Tao Wang","doi":"10.1109/ICINFA.2016.7831963","DOIUrl":null,"url":null,"abstract":"This paper presents a new algorithm of parameter and state estimation based on the Modified Cooperative Particle Swarm Optimization (MCPSO). Through modern control theory, the convergence and parameters setting rule of the algorithm is analyzed and a good optimization performance is shown from the given test functions. By minimizing the estimation states error covariance matrix for canonical state space models, the system states are computed by using the estimated parameters. Finally, a valuable simulation example is provided to show the validity and robustness of the new proposed algorithm.","PeriodicalId":389619,"journal":{"name":"2016 IEEE International Conference on Information and Automation (ICIA)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE International Conference on Information and Automation (ICIA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICINFA.2016.7831963","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This paper presents a new algorithm of parameter and state estimation based on the Modified Cooperative Particle Swarm Optimization (MCPSO). Through modern control theory, the convergence and parameters setting rule of the algorithm is analyzed and a good optimization performance is shown from the given test functions. By minimizing the estimation states error covariance matrix for canonical state space models, the system states are computed by using the estimated parameters. Finally, a valuable simulation example is provided to show the validity and robustness of the new proposed algorithm.
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