الصفحة 1
الصفحة 1
Filtering Theory : With Applications to Fault Detection, Isolation, and Estimation
The focus of this book is on filtering for linear processes, and its primary goal is to design filters from a class of linear stable unbiased filters that yield an estimation error with the lowest root-mean-square (RMS) norm. Various hierarchical classes of filtering problems are defined based on the availability of statistical knowledge regarding noise, disturbances, and other uncertainties. An important characteristic of the approach employed in this work for several aspects of filter analysis and design is structural in nature, revealing an inherent freedom to incorporate other classical secondary engineering constraints—such as placement of filter poles at desired locations—in filter design. Such a structural approach requires an understanding of powerful tools that then may be used in several engineering applications besides filtering.
Discrete-time Markov jump linear systems
Safety critical and high-integrity systems, such as industrial plants and economic systems, can be subject to abrupt changes - for instance, due to component or interconnection failure, sudden environment changes, etc. Combining probability and operator theory, Discrete-Time Markov Jump Linear Systems provides a unified and rigorous treatment of recent results for the control theory of discrete jump linear systems, which are used in these areas of application. The book is designed for experts in linear systems with Markov jump parameters, but is also of interest for specialists in stochastic control since it presents stochastic control problems for which an explicit solution is possible - making the book suitable for course use.
