In this monograph the authors develop a theory for the robust
control of discrete-time stochastic systems, subjected to both
independent random perturbations and to Markov chains. Such systems
are widely used to provide mathematical models for real processes
in fields such as aerospace engineering, communications,
manufacturing, finance and economy. The theory is a continuation of
the authors work presented in their previous book entitled
"Mathematical Methods in Robust Control of Linear Stochastic
Systems" published by Springer in 2006.
Key features:
- Provides a common unifying framework for discrete-time
stochastic systems corrupted with both independent random
perturbations and with Markovian jumps which are usually treated
separately in the control literature;
- Covers preliminary material on probability theory, independent
random variables, conditional expectation and Markov chains;
- Proposes new numerical algorithms to solve coupled matrix
algebraic Riccati equations;
- Leads the reader in a natural way to the original results
through a systematic presentation;
- Presents new theoretical results with detailed numerical
examples.
The monograph is geared to researchers and graduate students in
advanced control engineering, applied mathematics, mathematical
systems theory and finance. It is also accessible to undergraduate
students with a fundamental knowledge in the theory of stochastic
systems."
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