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The book covers the necessary pre-requisites from probability theory, stochastic processes, stochastic integrals and stochastic differential equations. It includes detailed treatment of the fundamental properties of stochastic systems subjected both to multiplicative white noise and to jump Markovian perturbations. Systematic presentation leads the reader in a natural way to the original results. New theoretical results accompanied by detailed numerical examples, and the book proposes new numerical algorithms to solve coupled matrix algebraic Riccati equations.
List of contents
Preface.- Preliminaries to Probability Theory and Stochastic Differential Equations.- Exponential Stability and Lyapunov-Type Equations.- Structural Properties of Linear Stochastic Systems.- The Riccati Equations of Stochastic Control.- Linear Quadratic Control Problem for Linear Stochastic Systems.- Stochastic Version of Bounded Real Lemma and Applications.- Robust Stabilization of Linear Stochastic Systems.- Bibliography.- Index.
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From the reviews:
"The subject of the book is related to the development of a theory of linear stochastic systems including both white noise and jump Markov perturbations, and to the development of analysis and design methods for linear-quadratic control, robust stabilization and disturbance attenuation problems. The book addresses graduate students and researchers in advanced control engineering, applied mathematics, mathematical systems theory and finance." (Vladimir Sobolev, Zentralblatt MATH, Vol. 1101 (3), 2007)
"This book is concerned with robust control of stochastic systems. One of the main features is its coverage of jump Markovian systems. Overall, this book presents results taking into consideration both white noise and Markov chain perturbations. It is clearly written and should be useful for people working in applied mathematics and in control and systems theory. The references cited provide further reading sources." (George Yin, Mathematical Reviews, Issue 2007 m)
"This book considers linear time varying stochastic systems, subjected to white noise disturbances and system parameter Markovian jumping, in the context of optimal control robust stabilization, and disturbance attenuation. The material presented in the book is organized in seven chapters. The book is very well written and organized. is a valuable reference for all researchers and graduate students in applied mathematics and control engineering interested in linear stochastic time varying control systems with Markovian parameter jumping and white noise disturbances." (Zoran Gajic, SIAM Review, Vol. 49 (3), 2007)
