Springer Mathematical Methods in Robust Control of Linear Stochastic Systems: 50 (Mathematical Concepts and Methods in Science and Engineering)

Linear stochastic systems are successfully used to provide mathematical models for real processes. This book covers the necessary pre-requisites from probability theory, stochastic processes, stochastic integrals and stochastic differential equations. Review 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)