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eBay-Artikelnr.:396596201899
Artikelmerkmale
- Artikelzustand
- subject_code
- TJFM
- subject
- Automotive technology and trades
- target_audience
- General/trade
- is_adult_product
- false
- edition_number
- 2
- binding
- paperback
- MPN
- N/A
- batteries_required
- false
- manufacturer
- Springer
- Brand
- Springer
- series_title
- Communications and Control Engineering
- number_of_items
- 1
- pages
- 472
- publication_date
- 2018-06-28T00:00:01Z
- unspsc_code
- 55101500
- batteries_included
- false
- ISBN
- 9783319834238
Über dieses Produkt
Product Identifiers
Publisher
Springer International Publishing A&G
ISBN-10
3319834231
ISBN-13
9783319834238
eBay Product ID (ePID)
12038393770
Product Key Features
Edition
2
Book Title
Nonlinear Model Predictive Control : Theory and Algorithms
Number of Pages
Xiv, 456 Pages
Language
English
Topic
Automation, Mechanics / Dynamics, General, System Theory, Electrical, Chemistry / Industrial & Technical
Publication Year
2018
Illustrator
Yes
Genre
Technology & Engineering, Science, Business & Economics
Book Series
Communications and Control Engineering Ser.
Format
Trade Paperback
Dimensions
Item Weight
280 Oz
Item Length
9.3 in
Item Width
6.1 in
Additional Product Features
Reviews
From the reviews of the first edition: "The book provides an excellent and extensive treatment of NMPC from a careful introduction to the underlying theory to advanced results. It can be used for independent reading by applied mathematicians, control theoreticians and engineers who desire a rigorous introduction into the NMPC theory. It can also be used as a textbook for a graduate-level university course in NMPC." (Ilya Kolmanovsky, Mathematical Reviews, April, 2015) "In the monograph nonlinear, discrete-time, finite-dimensional control systems with constant parameters are considered. ... Each chapter of the monograph contains many numerical examples which illustrate the theoretical considerations, several possible extensions and open problems. Moreover, relationships to results on predictive control published in the literature are pointed out." (Jerzy Klamka, Zentralblatt MATH, Vol. 1220, 2011)
Number of Volumes
1 vol.
Table Of Content
Introduction.- 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.
Synopsis
Nonlinear model predictive control (NMPC) is widely used in the process and chemical industries and increasingly for applications, such as those in the automotive industry, which use higher data sampling rates. Nonlinear Model Predictive Control is a thorough and rigorous introduction to NMPC for discrete-time and sampled-data systems. 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. NMPC schemes with and without stabilizing terminal constraints are detailed and intuitive examples illustrate the performance of different NMPC variants. An introduction to nonlinear optimal control algorithms gives insight into how the nonlinear optimisation routine - the core of any NMPC controller - works. An appendix covering NMPC software and accompanying software in MATLAB® and C++(downloadable from www.springer.com/ISBN) enables readers to perform computer experiments exploring the possibilities and limitations of NMPC. Nonlinear Model Predictive Control is primarily aimed at academic researchers and practitioners working in nonlinear control but the text is self-contained featuring background material on infinite-horizon optimal control and Lyapunov stability theory which makes the book accessible to graduate and advanced undergraduate students of control engineering and applied mathematics., NMPC software both in MATLAB® (for smaller academic examples) and C++ (for larger examples) will be provided., This 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(R) and C]+ (downloadable from extras.springer.com/), together with an explanatory appendix in the book itself, enables readers to perform computer experiments exploring thepossibilities and limitations of NMPC. The 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., This 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 thepossibilities and limitations of NMPC. The 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., Nonlinear model predictive control (NMPC) is widely used in the process and chemical industries and increasingly for applications, such as those in the automotive industry, which use higher data sampling rates. Nonlinear Model Predictive Control is a thorough and rigorous introduction to NMPC for discrete-time and sampled-data systems. 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. NMPC schemes with and without stabilizing terminal constraints are detailed and intuitive examples illustrate the performance of different NMPC variants. An introduction to nonlinear optimal control algorithms gives insight into how the nonlinear optimisation routine - the core of any NMPC controller - works. An appendix covering NMPC software and accompanying software in MATLAB(R) and C++(downloadable from www.springer.com/ISBN) enables readers to perform computer experiments exploring the possibilities and limitations of NMPC. Nonlinear Model Predictive Control is primarily aimed at academic researchers and practitioners working in nonlinear control but the text is self-contained featuring background material on infinite-horizon optimal control and Lyapunov stability theory which makes the book accessible to graduate and advanced undergraduate students of control engineering and applied mathematics.
LC Classification Number
TJ212-225
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