Data Driven Strategies
Theory and Applications
- Available for pre-order on March 10, 2023. Item will ship after March 31, 2023
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A key challenge in science and engineering is to provide a quantitative description of the systems under investigation, leveraging collected noisy data collected. Such a description may be a complete mathematical model or a mechanism to return controllers corresponding to new, unseen inputs. Recent advances in the theories are described in detail, along with their applications in engineering. The book aims to develop model-free system analysis and control strategies, i.e., data-driven control from theoretical analysis and engineering applications based only on measured data. The study aims to develop system identification, and combination in advanced control theory, i.e., data-driven control strategy as system and controller are generated from measured data directly. The book reviews the development of system identification and its combination in advanced control theory, i.e., data-driven control strategy, as they all depend on measured data. Firstly, data-driven identification is developed for the closed-loop, nonlinear system and model validation, i.e., obtaining model descriptions from measured data. Secondly, the data-driven idea is combined with some control strategies to be considered data-driven control strategies, such as data-driven model predictive control, data-driven iterative tuning control, and data-driven subspace predictive control. Thirdly data-driven identification and data-driven control strategies are applied to interested engineering. In this context, the book provides algorithms to perform state estimation of dynamical systems from noisy data and some convex optimization algorithms through identification and control problems.
Table of Contents
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.
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.