{"title":"基于多重经验模型的非线性过程系统的经济模型预测控制","authors":"Anas Alanqar, M. Ellis, P. Christofides","doi":"10.1109/ACC.2015.7172110","DOIUrl":null,"url":null,"abstract":"Economic model predictive control (EMPC) is a feedback control technique that attempts to tightly integrate economic optimization and feedback control since it is a predictive control scheme that is formulated with an objective function representing the process economics. As its name implies, EMPC requires the availability of a dynamic model to compute its control actions and such a model may be obtained either through application of first-principles or though system identification techniques. However, in industrial practice, it may be difficult in general to obtain an accurate first-principles model of the process. Motivated by this, in the present work, Lyapunov-based economic model predictive control (LEMPC) is designed with multiple linear empirical models. The different models are used to more accurately predict the behavior of a nonlinear system over a larger state-space region compared to using a single empirical linear model only. The LEMPC scheme is applied to a chemical process example to demonstrate its closed-loop stability and performance properties as well as significant computational advantages.","PeriodicalId":223665,"journal":{"name":"2015 American Control Conference (ACC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Economic model predictive control of nonlinear process systems using multiple empirical models\",\"authors\":\"Anas Alanqar, M. Ellis, P. Christofides\",\"doi\":\"10.1109/ACC.2015.7172110\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Economic model predictive control (EMPC) is a feedback control technique that attempts to tightly integrate economic optimization and feedback control since it is a predictive control scheme that is formulated with an objective function representing the process economics. As its name implies, EMPC requires the availability of a dynamic model to compute its control actions and such a model may be obtained either through application of first-principles or though system identification techniques. However, in industrial practice, it may be difficult in general to obtain an accurate first-principles model of the process. Motivated by this, in the present work, Lyapunov-based economic model predictive control (LEMPC) is designed with multiple linear empirical models. The different models are used to more accurately predict the behavior of a nonlinear system over a larger state-space region compared to using a single empirical linear model only. The LEMPC scheme is applied to a chemical process example to demonstrate its closed-loop stability and performance properties as well as significant computational advantages.\",\"PeriodicalId\":223665,\"journal\":{\"name\":\"2015 American Control Conference (ACC)\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 American Control Conference (ACC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ACC.2015.7172110\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 American Control Conference (ACC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ACC.2015.7172110","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Economic model predictive control of nonlinear process systems using multiple empirical models
Economic model predictive control (EMPC) is a feedback control technique that attempts to tightly integrate economic optimization and feedback control since it is a predictive control scheme that is formulated with an objective function representing the process economics. As its name implies, EMPC requires the availability of a dynamic model to compute its control actions and such a model may be obtained either through application of first-principles or though system identification techniques. However, in industrial practice, it may be difficult in general to obtain an accurate first-principles model of the process. Motivated by this, in the present work, Lyapunov-based economic model predictive control (LEMPC) is designed with multiple linear empirical models. The different models are used to more accurately predict the behavior of a nonlinear system over a larger state-space region compared to using a single empirical linear model only. The LEMPC scheme is applied to a chemical process example to demonstrate its closed-loop stability and performance properties as well as significant computational advantages.