Springer Event-Based State Estimation: A Stochastic Perspective: 41 (Studies in Systems, Decision and Control, 41)

Product Description This book explores event-based estimation problems. It shows how several stochastic approaches are developed to maintain estimation performance when sensors perform their updates at slower rates only when needed. The self-contained presentation makes this book suitable for readers with no more than a basic knowledge of probability analysis, matrix algebra and linear systems. The introduction and literature review provide information, while the main content deals with estimation problems from four distinct angles in a stochastic setting, using numerous illustrative examples and comparisons. The text elucidates both theoretical developments and their applications, and is rounded out by a review of open problems.  This book is a valuable resource for researchers and students who wish to expand their knowledge and work in the area of event-triggered systems. At the same time, engineers and practitioners in industrial process control will benefit from the event-triggering technique that reduces communication costs and improves energy efficiency in wireless automation applications.   Review “This book addresses essentially the engineers (or researchers and students interested in the area of event-trigged systems). The subject turns around event-based estimation problems in a stochastic setting. This document is self-contained and it is readable with just a basic knowledge of probability theory, Kalman filtering theory, and linear algebra. … This book is clear and well written. The results presented are proven, and each chapter contains notes and references.” (Bénédicte Puig, zbMATH 1331.62010, 2016) From the Back Cover This book explores event-based estimation problems. It shows how several stochastic approaches are developed to maintain estimation performance when sensors perform their updates at slower rates only when needed. The self-contained presentation makes this book suitable for readers with no more than a basic knowledge of probability analysis, matrix algebra and linear systems. The introduction and literature review provide information, while the main content deals with estimation problems from four distinct angles in a stochastic setting, using numerous illustrative examples and comparisons. The text elucidates both theoretical developments and their applications, and is rounded out by a review of open problems.  This book is a valuable resource for researchers and students who wish to expand their knowledge and work in the area of event-triggered systems. At the same time, engineers and practitioners in industrial process control will benefit from the event-triggering technique that reduces communication costs and improves energy efficiency in wireless automation applications.   About the Author Dawei Shi was born in Shandong Province, China. He received his B.Eng. degree in Electrical Engineering and its Automation from the Beijing Institute of Technology in 2008. In 2010, he received the financial support from the China Scholarship Council and the Provost Scholarship from the University of Alberta, where he received the Ph.D. degree on control systems in November 2014. From December 2014, he has been appointed as an Associate Professor at the School of Automation, Beijing Institute of Technology, China. In 2009, he received the Best Student Paper Award at the IEEE International Conference on Automation and Logistics.  His research interests include event-based control and estimation, robust model predictive control and tuning, and wireless sensor networks. He has authored or co-authored 18 papers, most of which have been published in the top journals and conferences in the field of control systems. He also applied for a US patent as the first inventor in March 2014, supported by Honeywell Process Solutions, Vancouver. He is a reviewer for a number of international journals and conferences, including Automatica, IEEE Transactions on Automatic Control, Sys