Finding exciting and efficient ways to integrate data into control theory has been a problem of great interest. As most of the classical contributions in control strategy rely on model description, the issue of finding such a model from measured data, i.e., system identification, has become mature research filed.
Finding exciting and efficient ways to integrate data into control theory has been a problem of great interest. As most of the classical contributions in control strategy rely on model description, the issue of finding such a model from measured data, i.e., system identification, has become mature research filed.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Wang Jianhong received a Ph.D. degree from Nanjing University of Aeronautics and Astronautics, China in 2011. From 2013 to 2015, he was a postdoctoral fellow in Informazione Politecnico di Milano. From 2016 to 2018, he was a professor at the University of Seville. He is currently a professor at Tecnológico de Monterrey. His current research interests include real-time and distributed control, optimization and system identification. Ricardo A. Ramirez-Mendoza received a Ph.D. degree from INPG, France in 1997. He is now a Professor and dean of Research at Tecnológico de Monterrey. His main research interests include applications of advanced control to automotive sysetms. He is the author of 3 books and more than 100 papers in top journals. He has worked as an expert consulting for different industries and is a certified reviewer for regional development projects. Ruben Morales-Menendez received his Ph.D. degree from the Tecnológico de Monterrey, Mexico, in 2003. He has been a specialist consultant in the analysis and design of automatic control systems for continuous processes for more than 35 years. He is currently dean of graduate studies, a member of the National System of Researchers of Mexico (Level II), of the Mexican Academy of Sciences and of the Mexico Academy of Engineering.
Inhaltsangabe
Introduction. Data driven model predictive control. Data driven identification for closed loop system. Data driven model validation for closed loop system. Data driven identification for nonlinear system. Data driven iterative tuning control. Data driven applications. Data driven subspace prediction control. Conclusions and outlook.
Introduction. Data driven model predictive control. Data driven identification for closed loop system. Data driven model validation for closed loop system. Data driven identification for nonlinear system. Data driven iterative tuning control. Data driven applications. Data driven subspace prediction control. Conclusions and outlook.
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