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Stochastic switching systems represent an interesting class of systems that can be used to model a variety of systems having abrupt random changes in their dynamics. Such systems may be found in the fields of manufacturing, communications, aerospace, power, and economics.
This work presents stochastic switching systems and provides up-to-date methods and techniques for the analysis and design of various control systems with or without uncertainties. An introductory chapter highlights basic concepts and practical models, which are then used to solve more advanced problems throughout the book. Included are many numerical examples as well as LMI analysis methods and design approaches to supplement the developed results.
Stochastic Switching Systems may be used as a supplementary textbook for graduate-level engineering courses, or as a reference for control engineers, graduate students, and researchers in systems and control. Prerequisites include elementary courses in matrix theory, probability, optimization techniques, and control systems theory.
This work presents stochastic switching systems and provides up-to-date methods and techniques for the analysis and design of various control systems with or without uncertainties. An introductory chapter highlights basic concepts and practical models, which are then used to solve more advanced problems throughout the book. Included are many numerical examples as well as LMI analysis methods and design approaches to supplement the developed results.
Stochastic Switching Systems may be used as a supplementary textbook for graduate-level engineering courses, or as a reference for control engineers, graduate students, and researchers in systems and control. Prerequisites include elementary courses in matrix theory, probability, optimization techniques, and control systems theory.
From the reviews:
"The text contains a large number of simple examples. The appendix collects together results from linear systems theory and probability, which may be helpful in understanding the material. The bibliography...is accurate and up-to-date. The treatment of robust stability and stabilization is carried out in parallel with the corresponding results for nominal systems models; this feature will be particularly useful to practicing engineers. Also, almost all of the results are expressed in terms of LMIs and/or their solutions; this is a unifying characteristic and is indeed advantageous on account of readily available complete catalog of the results in the area of MLSs; practicing engineers, in particular, will find it useful." -- Mathematical Reviews
"Boukas defines a stochastic switching system as a linear control problem with dynamics partially determined by a finite-state Markov process. ... the book may appeal to experts in control theory who already have intuition about linear control systems and may understand the results without needing to peruse the derivations." (Peter C. Kiessler, Journal of the American Statistical Association, Vol. 103 (481), 2008)
"The text contains a large number of simple examples. The appendix collects together results from linear systems theory and probability, which may be helpful in understanding the material. The bibliography...is accurate and up-to-date. The treatment of robust stability and stabilization is carried out in parallel with the corresponding results for nominal systems models; this feature will be particularly useful to practicing engineers. Also, almost all of the results are expressed in terms of LMIs and/or their solutions; this is a unifying characteristic and is indeed advantageous on account of readily available complete catalog of the results in the area of MLSs; practicing engineers, in particular, will find it useful." -- Mathematical Reviews
"Boukas defines a stochastic switching system as a linear control problem with dynamics partially determined by a finite-state Markov process. ... the book may appeal to experts in control theory who already have intuition about linear control systems and may understand the results without needing to peruse the derivations." (Peter C. Kiessler, Journal of the American Statistical Association, Vol. 103 (481), 2008)