{"title":"用序贯参数优化方法优化模型预测控制","authors":"A. Davtyan, Stefan Hoffmann, R. Scheuring","doi":"10.1109/CICA.2011.5945754","DOIUrl":null,"url":null,"abstract":"A methodology is developed for automatically tuning the main parameters of model predictive control (MPC) such as prediction horizon, control horizon and control interval. The tuning of parameters is done by means of sequential parameter optimization. In the process of optimization one of the major issues is the choice of an objective function. Several types of objective functions are tested in order to choose the one which solves the MPC tuning problem most adequate. In addition, different scenarios are analyzed if an exact model of the true plant does not exist.","PeriodicalId":420555,"journal":{"name":"Computational Intelligence in Control and Automation (CICA)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Optimization of model predictive control by means of sequential parameter optimization\",\"authors\":\"A. Davtyan, Stefan Hoffmann, R. Scheuring\",\"doi\":\"10.1109/CICA.2011.5945754\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A methodology is developed for automatically tuning the main parameters of model predictive control (MPC) such as prediction horizon, control horizon and control interval. The tuning of parameters is done by means of sequential parameter optimization. In the process of optimization one of the major issues is the choice of an objective function. Several types of objective functions are tested in order to choose the one which solves the MPC tuning problem most adequate. In addition, different scenarios are analyzed if an exact model of the true plant does not exist.\",\"PeriodicalId\":420555,\"journal\":{\"name\":\"Computational Intelligence in Control and Automation (CICA)\",\"volume\":\"21 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-04-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computational Intelligence in Control and Automation (CICA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CICA.2011.5945754\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Intelligence in Control and Automation (CICA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CICA.2011.5945754","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Optimization of model predictive control by means of sequential parameter optimization
A methodology is developed for automatically tuning the main parameters of model predictive control (MPC) such as prediction horizon, control horizon and control interval. The tuning of parameters is done by means of sequential parameter optimization. In the process of optimization one of the major issues is the choice of an objective function. Several types of objective functions are tested in order to choose the one which solves the MPC tuning problem most adequate. In addition, different scenarios are analyzed if an exact model of the true plant does not exist.
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