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Adaptive general predictive control using optimally scheduled multiple models for parallel-coursing utility units with a header / Lei Pan in Transactions of the ASME . Journal of dynamic systems, measurement, and control, Vol. 134 N° 4 (Juillet 2012) 

Public ISBD [article]  in Transactions of the ASME . Journal of dynamic systems, measurement, and control > Vol. 134 N° 4 (Juillet 2012) . - 09 p. Titre : Adaptive general predictive control using optimally scheduled multiple models for parallel-coursing utility units with a header Type de document : texte imprimé Auteurs : Lei Pan, Auteur ; Jiong Shen, Auteur ; Luh, Peter B., Auteur Année de publication : 2012 Article en page(s) : 09 p. Note générale : Dynamic systems Langues : Anglais (eng) Mots-clés : Adaptive  model  General  predictive  control  Fuzzy  multiple  model  Thermal  power  plant  Pressure  control Index. décimale : 629.8 Résumé : An adaptive general predictive control using optimally scheduled multiple models (OSMM-GPC) is presented for improving the load-following capability and economic profits of the system of parallel-coursing utility units with a header (PUUH). OSMM-GPC is a comprehensive control algorithm built on the distributed multiple-model control architecture. It is improved from general predictive control by two novel algorithms. One is the mixed fuzzy recursive least-squares (MFRLS) estimation and the other is the model optimally scheduling algorithm. The MFRLS mixes the local and global online estimations by weighting a dynamic multi-objective cost function on the membership feature of each sampling point. It provides better parameter estimation on the Takagi–Sugeno (TS) fuzzy model of a time-varying system than the local and global recursive least squares, thus, it is proper for building adaptive models for the OSMM-GPC. Based on high-precision adaptive models estimated by the MFRLS, the model optimally scheduling algorithm computes the regulating efficiencies of all control groups and then chooses the optimal one in charge of the multiple-variable general predictive control. Through the model scheduling at each operation point, considerable fuel consumption can be saved; meanwhile, a better control performance is achieved. Besides PUUH, the OSMM-GPC can also work for other distributed multiple-model control applications. DEWEY : 629.8 ISSN : 0022-0434 En ligne : http://asmedl.org/getabs/servlet/GetabsServlet?prog=normal&id=JDSMAA000134000004 [...] [article] Adaptive general predictive control using optimally scheduled multiple models for parallel-coursing utility units with a header [texte imprimé] / Lei Pan, Auteur ; Jiong Shen, Auteur ; Luh, Peter B., Auteur . - 2012 . - 09 p. Dynamic systems Langues : Anglais (eng) in Transactions of the ASME . Journal of dynamic systems, measurement, and control > Vol. 134 N° 4 (Juillet 2012) . - 09 p. Mots-clés : Adaptive  model  General  predictive  control  Fuzzy  multiple  model  Thermal  power  plant  Pressure  control Index. décimale : 629.8 Résumé : An adaptive general predictive control using optimally scheduled multiple models (OSMM-GPC) is presented for improving the load-following capability and economic profits of the system of parallel-coursing utility units with a header (PUUH). OSMM-GPC is a comprehensive control algorithm built on the distributed multiple-model control architecture. It is improved from general predictive control by two novel algorithms. One is the mixed fuzzy recursive least-squares (MFRLS) estimation and the other is the model optimally scheduling algorithm. The MFRLS mixes the local and global online estimations by weighting a dynamic multi-objective cost function on the membership feature of each sampling point. It provides better parameter estimation on the Takagi–Sugeno (TS) fuzzy model of a time-varying system than the local and global recursive least squares, thus, it is proper for building adaptive models for the OSMM-GPC. Based on high-precision adaptive models estimated by the MFRLS, the model optimally scheduling algorithm computes the regulating efficiencies of all control groups and then chooses the optimal one in charge of the multiple-variable general predictive control. Through the model scheduling at each operation point, considerable fuel consumption can be saved; meanwhile, a better control performance is achieved. Besides PUUH, the OSMM-GPC can also work for other distributed multiple-model control applications. DEWEY : 629.8 ISSN : 0022-0434 En ligne : http://asmedl.org/getabs/servlet/GetabsServlet?prog=normal&id=JDSMAA000134000004 [...]