Data-Driven Modeling, Filtering and Control: Methods and applications (Control, Robotics and Sensors)

Using important examples, this book showcases the potential of the latest data-based and data-driven methodologies for filter and control design. It discusses the most important classes of dynamic systems, along with the statistical and set membership analysis and design frameworks. The scientific research in many engineering fields has been shifting from traditional first-principle-based to data-driven or evidence-based theories. The latter methods may enable better system design, based on more accurate and verifiable information. In the era of big data, IoT and cyber-physical systems, this subject is of growing importance, as data-driven approaches are key enablers to solve problems that could not be addressed by standard approaches. This book presents a number of innovative data-driven methodologies, complemented by significant application examples, to show the potential offered by the most recent advances in the field. Applicable across a range of disciplines, the topics discussed here will be of interest to scientists, engineers and students in automatic control and learning systems, automotive and aerospace engineering, electrical engineering and signal processing Research in the field of system identification and control has been shifting from traditional model-based to data-driven or evidence-based theories. The latter methods enable better designs based on more direct and accurate data-based information and verifiable data. In the era of big data, IoT, and cyber-physical systems, this subject is of growing importance, as data-driven approaches are key enablers to solve problems that could not be addressed by previous standard approaches. This book presents a number of innovative data-driven methodologies, complemented by significant application examples to show the potential offered by the most recent advances in the field