Piao Haiguo, Bai Lipeng, Ming Hengchao, Wang Yongkang, Cao Cheng
{"title":"SAR Power Controller Design Research Based On PID Neural Network Artificial Intelligence and CPSO Algorithm","authors":"Piao Haiguo, Bai Lipeng, Ming Hengchao, Wang Yongkang, Cao Cheng","doi":"10.1109/CISS57580.2022.9971339","DOIUrl":null,"url":null,"abstract":"The $S^{4}R$ Switch topology technology is used in SAR satellite power system which has nonlinear characteristics, and difficult to establish an accurate mathematical model for designing a satisfied controller. In this paper, developed an artificial intelligence design method based the Dynamic P-I-D Neurons Identification Model (DPIDNIM) and nonlinear auto regressive moving average (NARMA-L2) model. In order to verify the design method’s feasibility, used a typical non-linear time series named Mackey-Glass (MG) time series to confirm the accuracy and effective of the identification model. Then a cooperated-PSO-based PID neural network control strategy (PIDNNC) is given out. Research results indicate DPIDNIM-CPSO-Based Artificial Intelligence design method adapts to design a controller for SAR satellite power system","PeriodicalId":331510,"journal":{"name":"2022 3rd China International SAR Symposium (CISS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 3rd China International SAR Symposium (CISS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CISS57580.2022.9971339","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
The $S^{4}R$ Switch topology technology is used in SAR satellite power system which has nonlinear characteristics, and difficult to establish an accurate mathematical model for designing a satisfied controller. In this paper, developed an artificial intelligence design method based the Dynamic P-I-D Neurons Identification Model (DPIDNIM) and nonlinear auto regressive moving average (NARMA-L2) model. In order to verify the design method’s feasibility, used a typical non-linear time series named Mackey-Glass (MG) time series to confirm the accuracy and effective of the identification model. Then a cooperated-PSO-based PID neural network control strategy (PIDNNC) is given out. Research results indicate DPIDNIM-CPSO-Based Artificial Intelligence design method adapts to design a controller for SAR satellite power system