{"title":"基于模型跟随控制的非最小相位局部模型网络前馈控制","authors":"Julian Willkomm, K. Wulff, J. Reger","doi":"10.1109/CCTA.2018.8511378","DOIUrl":null,"url":null,"abstract":"We consider the feedforward control design for a local model network (LMN) with possibly non-minimumphase dynamics. For a class of LMN with a parametrisation typical for experimental identification we show that the existence of a flat output is a very restrictive assumption. Therefore we propose a model following control (MFC) structure for the online-generation of a feedforward control signal. This method allows for arbitrary reference signals and has good robustness properties with respect to model uncertainties. We verify our approach by a simulation example and compare with a standard method using model inversion. Applying the proposed approach to a nonlinear process with unstable zero-dynamics illustrates the attainable good performance results.","PeriodicalId":358360,"journal":{"name":"2018 IEEE Conference on Control Technology and Applications (CCTA)","volume":"43 8","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Feedforward Control for Non-Minimumphase Local Model Networks Using Model Following Control\",\"authors\":\"Julian Willkomm, K. Wulff, J. Reger\",\"doi\":\"10.1109/CCTA.2018.8511378\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We consider the feedforward control design for a local model network (LMN) with possibly non-minimumphase dynamics. For a class of LMN with a parametrisation typical for experimental identification we show that the existence of a flat output is a very restrictive assumption. Therefore we propose a model following control (MFC) structure for the online-generation of a feedforward control signal. This method allows for arbitrary reference signals and has good robustness properties with respect to model uncertainties. We verify our approach by a simulation example and compare with a standard method using model inversion. Applying the proposed approach to a nonlinear process with unstable zero-dynamics illustrates the attainable good performance results.\",\"PeriodicalId\":358360,\"journal\":{\"name\":\"2018 IEEE Conference on Control Technology and Applications (CCTA)\",\"volume\":\"43 8\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE Conference on Control Technology and Applications (CCTA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CCTA.2018.8511378\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE Conference on Control Technology and Applications (CCTA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCTA.2018.8511378","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Feedforward Control for Non-Minimumphase Local Model Networks Using Model Following Control
We consider the feedforward control design for a local model network (LMN) with possibly non-minimumphase dynamics. For a class of LMN with a parametrisation typical for experimental identification we show that the existence of a flat output is a very restrictive assumption. Therefore we propose a model following control (MFC) structure for the online-generation of a feedforward control signal. This method allows for arbitrary reference signals and has good robustness properties with respect to model uncertainties. We verify our approach by a simulation example and compare with a standard method using model inversion. Applying the proposed approach to a nonlinear process with unstable zero-dynamics illustrates the attainable good performance results.