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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One of the main problems in science and engineering is to provide a quantitative description of the systems under investigation, leveraging collected noisy data. 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 covers 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 diploma in Engineering Cybernetics from the University of Yun Nan, China, in 2007. In 2011, he received a Doctorate in Science from the College of Automation Engineering at Nanjing University of Aeronautics and Astronautics, China. He is currently a Professor at Jiangxi University of Science and Technology. He was a postdoctoral fellow in the 28th Research Institute of China Electronics Technology Group Corporation from 2013 to 2015. He was a visiting professor in Informazione Politecnico di Milano for a few months in 2016. He has authored more than 100 scientific articles. His current research interests include real-time and distributed control, optimization and system identification, and data-driven control. Wang Jianhong can be contacted at: [email protected]
Ricardo A. Ramirez-Mendoza received a Ph.D. degree from INPG, France in1997. He is now a Professor at the Tecnologico de Monterrey in the National School of Engineering and Science. His main research interests include applications of automatic control, biomedical signal processing, identification systems, advanced control to automotive systems, biomedical systems, and brain. He is the (co-)author of 3 books, more than 100 papers in top journals, and more than 300 international conference papers. He has worked as an expert consultant for different industries and is a certified reviewer for regional development projects (biomedical, manufacturing, automotive, aerospace, etc.). The Office of Dean Ramirez-Mendoza leads a broad portfolio, including leadership for strategic initiatives to foster and enhance research success, leadership for research centers and institutes, provision of support aimed at promoting new research programs, and forging inter-schools, inter-disciplinary, inter-agency research partnerships, oversight for inter-school research centers and institutes and leadership, provide leadership in attracting, developing and retaining research chairs, provide leadership in attracting distinguished professors, design and provide leadership in enhancing, fostering, promoting and developing new creative models for sustainability of Research Programs, such as industrial chairs, industrial professorship, presidential professorship chairs, long-term strategic alliance, etc. Act and be an apostle of the creation of knowledge to improve the school's academic quality and the international positioning of Tecnologico de Monterrey. Take responsibility for representing and enhancing understanding of the discovery plan on various external and internal University boards, committees, and forums. [email protected]
Ruben Morales Menendez obtained a B.Sc. Degree in Chemical Engineering and Systems, the M.Sc. Degrees in Process Systems and Automation and a Ph.D. Degree in Artificial Intelligence from Tecnológico de Monterrey, Mexico, in 1984, 1986, 1992, and 2003, respectively. He was a Visiting Scholar with the Laboratory of Computational Intelligence, University of British Columbia, Vancouver, BC, Canada, from 2000 to 2003. He has been a consultant specializing in analyzing and designing automatic control systems for continuous processes for more than 35 years. He is the Dean of Graduate Studies of the School of Engineering and Sciences at Tecnológico de Monterrey. His research areas are fault diagnosis, control systems, artificial intelligence applications, and educational systems in engineering. Dr. Morales-Menendez is a member of the National Researchers System of Mexico (Level II), the Mexican Academic of Sciences, and the Engineering Academic of México. Email: [email protected]