{"title":"Predictive control with fuzzy characterization of percentage of solids, particle size and power demand for minerals grinding","authors":"M. Orchard, A. Flores, C. Muñoz, A. Cipriano","doi":"10.1109/CCA.2001.973933","DOIUrl":null,"url":null,"abstract":"The application of fuzzy predictive control to solve the regulatory problem in mineral grinding plants is considered. The controlled variables are percentage of solids, particle sizes and power demand and the manipulated variables are water and fresh ore flows. The controller uses linear multivariable models and the fuzzy characterization of the controlled variables, to calculate the manipulated variables. Simulation results under typical disturbances show a better performance compared with the conventional predictive control.","PeriodicalId":365390,"journal":{"name":"Proceedings of the 2001 IEEE International Conference on Control Applications (CCA'01) (Cat. No.01CH37204)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2001-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2001 IEEE International Conference on Control Applications (CCA'01) (Cat. No.01CH37204)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCA.2001.973933","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
The application of fuzzy predictive control to solve the regulatory problem in mineral grinding plants is considered. The controlled variables are percentage of solids, particle sizes and power demand and the manipulated variables are water and fresh ore flows. The controller uses linear multivariable models and the fuzzy characterization of the controlled variables, to calculate the manipulated variables. Simulation results under typical disturbances show a better performance compared with the conventional predictive control.
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