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008 151201s2016 sz | s |||| 0|eng d
020 _a9783319248530
_9978-3-319-24853-0
024 7 _a10.1007/978-3-319-24853-0
_2doi
050 4 _aTJ212-225
072 7 _aTJFM
_2bicssc
072 7 _aGPFC
_2bicssc
072 7 _aTEC004000
_2bisacsh
072 7 _aTJFM
_2thema
082 0 4 _a629.8312
_223
082 0 4 _a003
_223
100 1 _aKouvaritakis, Basil.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_954629
245 1 0 _aModel Predictive Control
_h[electronic resource] :
_bClassical, Robust and Stochastic /
_cby Basil Kouvaritakis, Mark Cannon.
250 _a1st ed. 2016.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2016.
300 _aXIII, 384 p. 54 illus., 3 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aAdvanced Textbooks in Control and Signal Processing,
_x2510-3814
505 0 _aFrom the Contents: Introduction -- Classical Model Predictive Control -- Robust Model Predictive Control with Additive Uncertainty: Open-loop Optimization Strategies -- Robust Model Predictive Control with Additive Uncertainty: Closed-loop Optimization Strategies.
520 _aFor the first time, a textbook that brings together classical predictive control with treatment of up-to-date robust and stochastic techniques. Model Predictive Control describes the development of tractable algorithms for uncertain, stochastic, constrained systems. The starting point is classical predictive control and the appropriate formulation of performance objectives and constraints to provide guarantees of closed-loop stability and performance. Moving on to robust predictive control, the text explains how similar guarantees may be obtained for cases in which the model describing the system dynamics is subject to additive disturbances and parametric uncertainties. Open- and closed-loop optimization are considered and the state of the art in computationally tractable methods based on uncertainty tubes presented for systems with additive model uncertainty. Finally, the tube framework is also applied to model predictive control problems involving hard or probabilistic constraints for the cases of multiplicative and stochastic model uncertainty. The book provides: extensive use of illustrative examples; sample problems; and discussion of novel control applications such as resource allocation for sustainable development and turbine-blade control for maximized power capture with simultaneously reduced risk of turbulence-induced damage. Graduate students pursuing courses in model predictive control or more generally in advanced or process control and senior undergraduates in need of a specialized treatment will find Model Predictive Control an invaluable guide to the state of the art in this important subject. For the instructor it provides an authoritative resource for the construction of courses.
650 0 _aControl engineering.
_931970
650 0 _aSystem theory.
_93409
650 0 _aControl theory.
_93950
650 0 _aChemistry, Technical.
_914638
650 0 _aAutomotive engineering.
_954630
650 0 _aAerospace engineering.
_96033
650 0 _aAstronautics.
_954631
650 1 4 _aControl and Systems Theory.
_931972
650 2 4 _aSystems Theory, Control .
_931597
650 2 4 _aIndustrial Chemistry.
_914640
650 2 4 _aAutomotive Engineering.
_954632
650 2 4 _aAerospace Technology and Astronautics.
_954633
700 1 _aCannon, Mark.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_954634
710 2 _aSpringerLink (Online service)
_954635
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319248516
776 0 8 _iPrinted edition:
_z9783319248523
776 0 8 _iPrinted edition:
_z9783319796895
830 0 _aAdvanced Textbooks in Control and Signal Processing,
_x2510-3814
_954636
856 4 0 _uhttps://doi.org/10.1007/978-3-319-24853-0
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c79395
_d79395