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Robust Filtering for Uncertain Systems - A Parameter-Dependent Approach (Paperback, Softcover reprint of the original 1st ed. 2014)
Loot Price: R3,788
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Robust Filtering for Uncertain Systems - A Parameter-Dependent Approach (Paperback, Softcover reprint of the original 1st ed. 2014)
Series: Communications and Control Engineering
Expected to ship within 10 - 15 working days
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This monograph provides the reader with a systematic treatment of
robust filter design, a key issue in systems, control and signal
processing, because of the fact that the inevitable presence of
uncertainty in system and signal models often degrades the
filtering performance and may even cause instability. The methods
described are therefore not subject to the rigorous assumptions of
traditional Kalman filtering. The monograph is concerned with
robust filtering for various dynamical systems with parametric
uncertainties and focuses on parameter-dependent approaches to
filter design. Classical filtering schemes, like H2 filtering and
HY filtering, are addressed and emerging issues such as robust
filtering with constraints on communication channels and signal
frequency characteristics are discussed. The text features: *
design approaches to robust filters arranged according to varying
complexity level and emphasizing robust filtering in the
parameter-dependent framework for the first time; * guidance on the
use of special realistic phenomena or factors to describe problems
more accurately and to improve filtering performance; * a unified
linear matrix inequality formulation of design approaches for easy
and effective filter design; * demonstration of the techniques of
matrix decoupling technique, the generalized
Kalman-Yakubovich-Popov lemma, the free weighting matrix technique
and the delay modelling approach, in robust filtering; * numerous
easy-to-follow simulation examples, graphical and tabular
illustrations to help the reader understand the filter design
approaches developed; and * an account of emerging issues on robust
filtering for research to inspire future investigation. Robust
Filtering for Uncertain Systems will be of interest to academic
researchers specializing in linear, robust and optimal control and
estimation and to practitioners working in tracking and network
control or signal filtering, detection and estimation. Graduate
students learning control and systems theory, signal processing or
applied mathematics will also find the book to be a valuable
resource.
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