000			04436nam a22005415i 4500
999			_c11812 _d11812
001			978-3-319-41108-8
003			DE-He213
005			20211206111532.0
008			160727s2017 gw | s |||| 0|eng d
020			_a9783319411088
040			_cAIKTC-KRRC
041			_aENG
072		7	_aTJFM _2bicssc
072		7	_aTEC004000 _2bisacsh
072		7	_aTJFM _2thema
082	0	4	_a629.8 _223
100	1		_aEllis, Matthew. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aEconomic Model Predictive Control _h[electronic resource] : _bTheory, Formulations and Chemical Process Applications /
250			_a1st ed. 2017.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2017.
300			_aXXIV, 292 p. 95 illus., 16 illus. in color. _bCard Paper
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aAdvances in Industrial Control, _x1430-9491
520			_aThis book presents general methods for the design of economic model predictive control (EMPC) systems for broad classes of nonlinear systems that address key theoretical and practical considerations including recursive feasibility, closed-loop stability, closed-loop performance, and computational efficiency. Specifically, the book proposes: Lyapunov-based EMPC methods for nonlinear systems; two-tier EMPC architectures that are highly computationally efficient; and EMPC schemes handling explicitly uncertainty, time-varying cost functions, time-delays and multiple-time-scale dynamics. The proposed methods employ a variety of tools ranging from nonlinear systems analysis, through Lyapunov-based control techniques to nonlinear dynamic optimization. The applicability and performance of the proposed methods are demonstrated through a number of chemical process examples. The book presents state-of-the-art methods for the design of economic model predictive control systems for chemical processes. In addition to being mathematically rigorous, these methods accommodate key practical issues, for example, direct optimization of process economics, time-varying economic cost functions and computational efficiency. Numerous comments and remarks providing fundamental understanding of the merging of process economics and feedback control into a single framework are included. A control engineer can easily tailor the many detailed examples of industrial relevance given within the text to a specific application. The authors present a rich collection of new research topics and references to significant recent work makingEconomic Model Predictive Control an important source of information and inspiration for academics and graduate students researching the area and for process engineers interested in applying its ideas.
650		0	_aMechanical Engineering _94626
653			_aControl and Systems Theory.
653			_aIndustrial Chemistry/Chemical Engineering.
653			_aProduction.
700	1		_aLiu, Jinfeng. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aChristofides, Panagiotis D. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
710	2		_aSpringerLink (Online service)
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9783319411071
776	0	8	_iPrinted edition: _z9783319411095
776	0	8	_iPrinted edition: _z9783319822686
830		0	_aAdvances in Industrial Control, _x1430-9491
856	4	0	_uhttps://doi.org/10.1007/978-3-319-41108-8 _zClick here to access eBook in Springer Nature platform. (Within Campus only.)
942			_cEBOOKS _2ddc