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020 _a9783319411088
_9978-3-319-41108-8
024 7 _a10.1007/978-3-319-41108-8
_2doi
050 4 _aTJ212-225
072 7 _aTJFM
_2bicssc
072 7 _aGPFC
_2bicssc
072 7 _aTEC004000
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072 7 _aTJFM
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082 0 4 _a629.8312
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082 0 4 _a003
_223
100 1 _aEllis, Matthew.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_954080
245 1 0 _aEconomic Model Predictive Control
_h[electronic resource] :
_bTheory, Formulations and Chemical Process Applications /
_cby Matthew Ellis, Jinfeng Liu, Panagiotis D. Christofides.
250 _a1st ed. 2017.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2017.
300 _aXXIV, 292 p. 95 illus., 16 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 _aAdvances in Industrial Control,
_x2193-1577
505 0 _aIntroduction -- Background on Nonlinear Systems, Control, and Optimization -- Brief Overview of EMPC Methods and some Preliminary Results -- Lyapunov-Based EMPC -- State Estimation and EMPC -- Two-Layer EMPC Systems -- EMPC Systems: Computational Efficiency and Real-Time Implementation.
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 _aControl engineering.
_931970
650 0 _aChemistry, Technical.
_914638
650 0 _aProduction management.
_99536
650 1 4 _aControl and Systems Theory.
_931972
650 2 4 _aIndustrial Chemistry.
_914640
650 2 4 _aProduction .
_932926
700 1 _aLiu, Jinfeng.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_954081
700 1 _aChristofides, Panagiotis D.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_954082
710 2 _aSpringerLink (Online service)
_954083
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,
_x2193-1577
_954084
856 4 0 _uhttps://doi.org/10.1007/978-3-319-41108-8
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c79283
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