A significant shortcoming of the state space control theory that emerged in the 1960s was its lack of concern for the issue of robustness. However, in the design of feedback control systems, robustness is a critical issue. These facts led to great activity in the research area of robust control theory. One of the major developments of modern control theory was the Kalman Filter and hence the development of a robust version of the Kalman Filter has become an active area of research. Although the issue of robustness in filtering is not as critical as in feedback control (where there is always the issue of instability to worry about), research on robust filtering and state estimation has remained very active in recent years. However, although numerous books have appeared on the topic of Kalman filtering, this book is one of the first to appear on robust Kalman filtering. Most of the material presented in this book derives from a period of research collaboration between the authors from 1992 to 1994. However, its origins go back earlier than that. The first author (LR. P. ) became in terested in problems of robust filtering through his research collaboration with Dr. Duncan McFarlane. At this time, Dr. McFarlane was employed at the Melbourne Research Laboratories ofBHP Ltd. , a large Australian min erals, resources, and steel processing company.

Autorentext

Daoyi Dong is currently a Scientia Associate Professor at the University of New South Wales, Canberra, Australia, and an Australian Research Council Future Fellow. His research interests include quantum control and machine learning. He has published more than 100 journal papers and more than 40 conference papers. Associate Professor Dong was awarded an ACA Temasek Young Educator Award by The Asian Control Association, a Humboldt Fellowship by Alexander von Humboldt Foundation, and is a recipient of a Future Fellowship, an International Collaboration Award and an Australian Post-Doctoral Fellowship from the Australian Research Council. He served as an Associate Editor of IEEE Transactions on Neural Networks and Learning Systems (2015-2021) and currently a Technical Editor of IEEE/ASME Transactions on Mechatronics, an Associate Editor of IEEE Transactions on Cybernetics, and a Guest Editor of Annual Reviews in Control. Ian R. Petersen is currently a professor at the Australian National University. He held an Australian Research Council Professorial Fellowship from 2005 to 2007, an Australian Research Council Federation Fellowship from 2007 to 2012, and an Australian Research Council Laureate Fellowship from 2012 to 2016. He has served as an Associate Editor for the IEEE Transactions on Automatic Control, Systems and Control Letters, Automatica, and SIAM Journal on Control and Optimization. Currently he is an Editor for Automatica in the area of optimization in systems and control. He is a fellow of the IFAC, the IEEE and the Australian Academy of Science. His main research interests are in robust control theory, quantum control theory and stochastic control theory. Ian Petersen was elected IFAC Council Member for the 2014-2017 and 2018-2021 Trienniums. He was also elected to be a member of the IEEE Control Systems Society Board of Governors for the periods 2011-2013 and 2015-2017. He was Vice-president for Technical Activity for the Asian Control Association and was General Chair of the 2012 Australia Control Conference. He was General Chair of the 2015 IEEE Multi-Conference on Systems and Control.

Inhalt
1 Introduction.- 2 Continuous-Time Quadratic Guaranteed Cost Filtering.- 3 Discrete-Time Quadratic Guaranteed Cost Filtering.- 4 Continuous-Time Set-Valued State Estimation and Model Validation.- 5 Discrete-Time Set-Valued State Estimation.- 6 Robust State Estimation with Discrete and Continuous Measurements.- 7 Set-Valued State Estimation with Structured Uncertainty.- 8 Robust H? Filtering with Structured Uncertainty.- 9 Robust Fixed Order H? Filtering.- 10 Set-Valued State Estimation for Nonlinear Uncertain Systems.- 11 Robust Filtering Applied to Induction Motor Control.- References.