{"title":"基于基本模型和多模型预测控制的比较","authors":"B. Aufderheide, Vinay Prasad, B. Bequette","doi":"10.1109/CDC.2001.980977","DOIUrl":null,"url":null,"abstract":"A multiple model strategy is implemented in a model predictive control framework. The model bank design requires minimal plant knowledge based on the ranges of gains, dominant time constants and time delays. The application example is the isothermal Van de Vusse reaction in a continuous stirred tank reactor, which exhibits challenging input multiplicity behavior. Disturbances include additive input and output noises and changes in system parameters. Results are compared with an extended Kalman filter (EKF)-based model predictive controller that uses a fundamental model with a disturbance parameter estimated online. The multiple model predictive controller performance is comparable to that demonstrated by the EKF-based model predictive controller.","PeriodicalId":131411,"journal":{"name":"Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2001-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"19","resultStr":"{\"title\":\"A comparison of fundamental model-based and multiple model predictive control\",\"authors\":\"B. Aufderheide, Vinay Prasad, B. Bequette\",\"doi\":\"10.1109/CDC.2001.980977\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A multiple model strategy is implemented in a model predictive control framework. The model bank design requires minimal plant knowledge based on the ranges of gains, dominant time constants and time delays. The application example is the isothermal Van de Vusse reaction in a continuous stirred tank reactor, which exhibits challenging input multiplicity behavior. Disturbances include additive input and output noises and changes in system parameters. Results are compared with an extended Kalman filter (EKF)-based model predictive controller that uses a fundamental model with a disturbance parameter estimated online. The multiple model predictive controller performance is comparable to that demonstrated by the EKF-based model predictive controller.\",\"PeriodicalId\":131411,\"journal\":{\"name\":\"Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228)\",\"volume\":\"24 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2001-12-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"19\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CDC.2001.980977\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CDC.2001.980977","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 19
摘要
在模型预测控制框架中实现了多模型策略。模型库的设计需要基于增益范围、主导时间常数和时间延迟的最小植物知识。应用实例为连续搅拌槽式反应器中的等温Van de Vusse反应,该反应具有挑战性的输入多重性。干扰包括输入输出加性噪声和系统参数的变化。结果与基于扩展卡尔曼滤波(EKF)的模型预测控制器进行了比较,该控制器使用在线估计扰动参数的基本模型。多模型预测控制器的性能与基于ekf的模型预测控制器相当。
A comparison of fundamental model-based and multiple model predictive control
A multiple model strategy is implemented in a model predictive control framework. The model bank design requires minimal plant knowledge based on the ranges of gains, dominant time constants and time delays. The application example is the isothermal Van de Vusse reaction in a continuous stirred tank reactor, which exhibits challenging input multiplicity behavior. Disturbances include additive input and output noises and changes in system parameters. Results are compared with an extended Kalman filter (EKF)-based model predictive controller that uses a fundamental model with a disturbance parameter estimated online. The multiple model predictive controller performance is comparable to that demonstrated by the EKF-based model predictive controller.