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020 _a9783319460246
_9978-3-319-46024-6
024 7 _a10.1007/978-3-319-46024-6
_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 _aGrüne, Lars.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_959119
245 1 0 _aNonlinear Model Predictive Control
_h[electronic resource] :
_bTheory and Algorithms /
_cby Lars Grüne, Jürgen Pannek.
250 _a2nd ed. 2017.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2017.
300 _aXIV, 456 p. 80 illus., 22 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 _aCommunications and Control Engineering,
_x2197-7119
505 0 _aIntroduction -- Discrete-Time and Sampled-Data Systems -- Nonlinear Model Predictive Control -- Infinite-Horizon Optimal Control -- Stability and Suboptimality Using Stabilizing Constraints -- Stability and Suboptimality Without Stabilizing Constraints -- Feasibility and Robustness -- Economic Nonlinear Model Predictive Control -- Distributed Nonlinear Model Predictive Control -- Variants and Extensions -- Numerical Discretization -- Numerical Optimal Control of Nonlinear Systems -- Appendix: NMPC Software Supporting This Book.
520 _aThis book offers readers a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems. NMPC schemes with and without stabilizing terminal constraints are detailed, and intuitive examples illustrate the performance of different NMPC variants. NMPC is interpreted as an approximation of infinite-horizon optimal control so that important properties like closed-loop stability, inverse optimality and suboptimality can be derived in a uniform manner. These results are complemented by discussions of feasibility and robustness. An introduction to nonlinear optimal control algorithms yields essential insights into how the nonlinear optimization routine—the core of any nonlinear model predictive controller—works. Accompanying software in MATLAB® and C++ (downloadable from extras.springer.com/), together with an explanatory appendix in the book itself, enables readers to perform computer experiments exploring the possibilities and limitations of NMPC. This book (second edition) has been substantially rewritten, edited and updated to reflect the significant advances that have been made since the publication of its predecessor, including: • a new chapter on economic NMPC relaxing the assumption that the running cost penalizes the distance to a pre-defined equilibrium; • a new chapter on distributed NMPC discussing methods which facilitate the control of large-scale systems by splitting up the optimization into smaller subproblems; • an extended discussion of stability and performance using approximate updates rather than full optimization; • replacement of the pivotal sufficient condition for stability without stabilizing terminal conditions with a weaker alternative and inclusion of an alternative and much simpler proof in the analysis; and • further variations and extensions in response to suggestions from readers of the first edition. Though primarily aimed at academic researchers and practitioners working in control and optimization, the text is self-contained, featuring background material on infinite-horizon optimal control and Lyapunov stability theory that also makes it accessible for graduate students in control engineering and applied mathematics.
650 0 _aControl engineering.
_931970
650 0 _aSystem theory.
_93409
650 0 _aControl theory.
_93950
650 0 _aChemistry, Technical.
_914638
650 0 _aAutomotive engineering.
_959120
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.
_959121
700 1 _aPannek, Jürgen.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_959122
710 2 _aSpringerLink (Online service)
_959123
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319460239
776 0 8 _iPrinted edition:
_z9783319460253
776 0 8 _iPrinted edition:
_z9783319834238
830 0 _aCommunications and Control Engineering,
_x2197-7119
_959124
856 4 0 _uhttps://doi.org/10.1007/978-3-319-46024-6
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c80283
_d80283