Nonlinear Model Predictive Control by Jrgen Pannek

Nonlinear Model Predictive Control by Jrgen Pannek

Nonlinear Model Predictive Control is a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems. An introduction to nonlinear optimal control algorithms gives insight into how the nonlinear optimisation routine the core of any NMPC controller works.

“The book provides an excellent and extensive treatment of NMPC from a careful introduction to the underlying theory to advanced resultsIt 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)

Lars Gr�ne has been Professor for Applied Mathematics at the University of Bayreuth, Germany, since 2002 and head of the Chair of Applied Mathematics since 2009. He received his Diploma and Ph.D. in Mathematics in 1994 and 1996, respectively, from the University of Augsburg and his habilitation from the J.W. Goethe University in Frankfurt/M in 2001. He held visiting positions at the Universities of Rome 'La Sapienza' (Italy), Padova (Italy), Melbourne (Australia), Paris IX -- Dauphine (France) and Newcastle (Australia). Professor Gr�ne is Editor-in-Chief of the journal Mathematics of Control, Signals and Systems (MCSS), Associate Editor for the Journal of Optimization Theory and Applications (JOTA) and the Journal of Applied Mathematica and Mechanics (ZAMM) and member of the Managing Board of the GAMM -- International Association of Applied Mathematics and Mechanics. Professor Gr�ne co-authored four books, more than 100 papers and chapters in peer reviewed journals and books and more than 80 articles in conference proceedings. He is member of the steering committee of the International Symposium on Mathematical Theory of Networks and Systems (MTNS) and member of the Program Comittees of various other conferences, including IFAC-NOLCOS symposia, the European Control Conference and the IEEE Conference on Decision and Control. In 2012, Professor Gr�ne was awarded the Excellence in Teaching Award (Preis f�r gute Lehre) from the State of Bavaria. His research interests lie in the area of mathematical systems and control theory with a focus on numerical and optimization-based methods for stability analysis and stabilization of nonlinear systems.

J�rgen Pannek has been Professor in the Department of Production Engineering at the University of Bremen (Germany) since 2014. He received his Diploma in Mathematical Economics and his Ph.D. in Mathematics from the University of Bayreuth in 2005 and 2009. He was visiting lecturer at the University of Birmingham (England) in 2008 and Curtin University of Perth (Australia) from 2010 to 2011. Thereafter, he worked as scientific assistant in the Department of Aerospace Engineering at the University of the Federal Armed Forces Munich (Germany). In his research, he focuses on the area of system and control theory from the application point of view regarding robotics, logistics and cyberphysical systems.

SKU	Nicht verfügbar
ISBN 13	9780857295002
ISBN 10	0857295004
Titel	Nonlinear Model Predictive Control
Autor	Jrgen Pannek
Serie	Communications And Control Engineering
Buchzustand	Nicht verfügbar
Bindungsart	Hardback
Verlag	Springer London Ltd
Erscheinungsjahr	2011-04-10
Seitenanzahl	360
Hinweis auf dem Einband	Die Abbildung des Buches dient nur Illustrationszwecken, die tatsächliche Bindung, das Cover und die Auflage können sich davon unterscheiden.
Hinweis	Nicht verfügbar