000 03586nam a22005055i 4500
001 978-3-642-33947-9
003 DE-He213
005 20200421111653.0
007 cr nn 008mamaa
008 120920s2013 gw | s |||| 0|eng d
020 _a9783642339479
_9978-3-642-33947-9
024 7 _a10.1007/978-3-642-33947-9
_2doi
050 4 _aTJ212-225
072 7 _aTJFM
_2bicssc
072 7 _aTEC004000
_2bisacsh
082 0 4 _a629.8
_223
100 1 _aKarer, Gorazd.
_eauthor.
245 1 0 _aPredictive Approaches to Control of Complex Systems
_h[electronic resource] /
_cby Gorazd Karer, Igor Škrjanc.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2013.
300 _aXII, 260 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Computational Intelligence,
_x1860-949X ;
_v454
505 0 _aIntroduction -- Modeling of complex systems for predictive control -- Modeling an identification of a batch reactor -- Predictive control of complex systems -- Predictive control of complex systems.
520 _aA predictive control algorithm uses a model of the controlled system to predict the system behavior for various input scenarios and determines the most appropriate inputs accordingly. Predictive controllers are suitable for a wide range of systems; therefore, their advantages are especially evident when dealing with relatively complex systems, such as nonlinear, constrained, hybrid, multivariate systems etc. However, designing a predictive control strategy for a complex system is generally a difficult task, because all relevant dynamical phenomena have to be considered. Establishing a suitable model of the system is an essential part of predictive control design. Classic modeling and identification approaches based on linear-systems theory are generally inappropriate for complex systems; hence, models that are able to appropriately consider complex dynamical properties have to be employed in a predictive control algorithm. This book first introduces some modeling frameworks, which can encompass the most frequently encountered complex dynamical phenomena and are practically applicable in the proposed predictive control approaches. Furthermore, unsupervised learning methods that can be used for complex-system identification are treated. Finally, several useful predictive control algorithms for complex systems are proposed and their particular advantages and drawbacks are discussed. The presented modeling, identification and control approaches are complemented by illustrative examples. The book is aimed towards researches and postgraduate students interested in modeling, identification and control, as well as towards control engineers needing practically usable advanced control methods for complex systems.
650 0 _aEngineering.
650 0 _aSystem theory.
650 0 _aComplexity, Computational.
650 0 _aControl engineering.
650 1 4 _aEngineering.
650 2 4 _aControl.
650 2 4 _aComplexity.
650 2 4 _aSystems Theory, Control.
700 1 _aŠkrjanc, Igor.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642339462
830 0 _aStudies in Computational Intelligence,
_x1860-949X ;
_v454
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-33947-9
912 _aZDB-2-ENG
942 _cEBK
999 _c54541
_d54541