000			04050nam a22005777a 4500
001			sulb-eb0024317
003			BD-SySUS
005			20160413122433.0
007			cr nn 008mamaa
008			121227s2013 gw | s |||| 0|eng d
020			_a9783642341007 _9978-3-642-34100-7
024	7		_a10.1007/978-3-642-34100-7 _2doi
050		4	_aQA273.A1-274.9
050		4	_aQA274-274.9
072		7	_aPBT _2bicssc
072		7	_aPBWL _2bicssc
072		7	_aMAT029000 _2bisacsh
082	0	4	_a519.2 _223
100	1		_aCosta, Oswaldo L.V. _eauthor.
245	1	0	_aContinuous-Time Markov Jump Linear Systems _h[electronic resource] / _cby Oswaldo L.V. Costa, Marcelo D. Fragoso, Marcos G. Todorov.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg : _bImprint: Springer, _c2013.
300			_aXII, 288 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aProbability and Its Applications, _x1431-7028
505	0		_a1.Introduction -- 2.A Few Tools and Notations -- 3.Mean Square Stability -- 4.Quadratic Optimal Control with Complete Observations -- 5.H2 Optimal Control With Complete Observations -- 6.Quadratic and H2 Optimal Control with Partial Observations -- 7.Best Linear Filter with Unknown (x(t), θ(t)) -- 8.H_$infty$ Control -- 9.Design Techniques -- 10.Some Numerical Examples -- A.Coupled Differential and Algebraic Riccati Equations -- B.The Adjoint Operator and Some Auxiliary Results -- References. - Notation and Conventions -- Index.
520			_aIt has been widely recognized nowadays the importance of introducing mathematical models that take into account possible sudden changes in the dynamical behavior of  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.
650		0	_aMathematics.
650		0	_aDynamics.
650		0	_aErgodic theory.
650		0	_aOperator theory.
650		0	_aSystem theory.
650		0	_aProbabilities.
650	1	4	_aMathematics.
650	2	4	_aProbability Theory and Stochastic Processes.
650	2	4	_aSystems Theory, Control.
650	2	4	_aDynamical Systems and Ergodic Theory.
650	2	4	_aOperator Theory.
700	1		_aFragoso, Marcelo D. _eauthor.
700	1		_aTodorov, Marcos G. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783642340994
830		0	_aProbability and Its Applications, _x1431-7028
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-642-34100-7
912			_aZDB-2-SMA
942			_2Dewey Decimal Classification _ceBooks
999			_c46409 _d46409