It has been widely recognized nowadays the importance of introducing
mathematical models that take into account possible sudden changes in
the dynamical behavior of a high-integrity systems or a safety-critical
system. Such systems can be found in aircraft control, nuclear power
stations, robotic manipulator systems, integrated communication networks
and large-scale flexible structures for space stations, and are
inherently vulnerable to abrupt changes in their structures caused by
component or interconnection failures. In this regard, a particularly
interesting class of models is the so-called Markov jump linear systems
(MJLS), which have been used in numerous applications including
robotics, economics and wireless communication. Combining probability
and operator theory, the present volume provides a unified and rigorous
treatment of recent results in control theory of continuous-time MJLS.
This unique approach is of great interest to experts working in the
field of linear systems with Markovian jump parameters or in stochastic
control. The volume focuses on one of the few cases of stochastic
control problems with an actual explicit solution and offers material
well-suited to coursework, introducing students to an interesting and
active research area.
The book is addressed to researchers working in control and signal
processing engineering. Prerequisites include a solid background in
classical linear control theory, basic familiarity with continuous-time
Markov chains and probability theory, and some elementary knowledge of
operator theory.
