Assessment and Future Directions of Nonlinear Model Predictive Control

  • Rolf Findeisen
  • Frank Allgöwer
  • Lorenz T. Biegler

Part of the Lecture Notes in Control and Information Sciences book series (LNCIS, volume 358)

Table of contents

  1. Front Matter
    Pages I-XII
  2. Foundations and History of NMPC

    1. Eduardo F. Camacho, Carlos Bordons
      Pages 1-16
  3. Theoretical Aspects of NMPC

    1. S. Emre Tuna, Ricardo G. Sanfelice, Michael J. Messina, Andrew R. Teel
      Pages 17-34
    2. John Anthony Rossiter, Bert Pluymers, Bart De Moor
      Pages 63-76
    3. Prashant Mhaskar, Nael H. El-Farra, Panagiotis D. Christofides
      Pages 77-91
    4. M. Lazar, W. P. M. H. Heemels, A. Bemporad, S. Weiland
      Pages 93-103
    5. L. Grüne, D. Nešić, J. Pannek
      Pages 105-113
    6. Fernando A. C. C. Fontes, Lalo Magni, Eva Gyurkovics
      Pages 115-129
    7. T. Alamo, M. Fiacchini, A. Cepeda, D. Limon, J. M. Bravo, E. F. Camacho
      Pages 131-139
    8. Meka Srinivasarao, Sachin C. Patwardhan, R. D. Gudi
      Pages 141-149
    9. Tobias Raff, Christian Ebenbauer, Prank Allgöwer
      Pages 151-162
  4. Numerical Aspects of NMPC

    1. Hans Georg Bock, Moritz Diehl, Peter Kühl, Ekaterina Kostina, Johannes P. Schiöder, Leonard Wirsching
      Pages 163-179
    2. Alexandra Grancharova, Tor A. Johansen, Petter Tøndel
      Pages 181-192
    3. V. Sakizlis, K. I. Kouramas, N. P. Faisca, E. N. Pistikopoulos
      Pages 193-205
    4. Adrian G. Wills, William P. Heath
      Pages 207-216
    5. Christopher E. Long, Edward P. Gatzke
      Pages 217-228
  5. Robustness, Robust Design, and Uncertainty

    1. Lalo Magni, Riccardo Scattolini
      Pages 239-254
    2. 1Mark Cannon, Paul Couchman, Basil Kouvaritakis
      Pages 255-268
    3. Jan M. Maciejowski, Andrea Lecchini Visintini, John Lygeros
      Pages 269-281
    4. Hong Chen, Xingquan Gao, Hu Wang, Rolf Findeisen
      Pages 283-294
    5. Lei Xie, Pu Li, Günter Wozny
      Pages 295-304
    6. Harvey Arellano-Garcia, Moritz Wendt, Tilman Barz, Guenter Wozny
      Pages 305-315
    7. D. Limon, T. Alamo, J. M. Bravo, E. F. Camacho, D. R. Ramirez, D. Muñoz de la Peña et al.
      Pages 317-326
    8. Rafail Gabasov, Faina M. Kirillova, Natalia M. Dmitruk
      Pages 327-334
  6. State Estimation and Output Feedback

    1. Angelo Alessandri, Marco Baglietto, Giorgio Battistelli
      Pages 347-358
    2. John Bagterp Jørgensen, Morten Rode Kristensen, Per Grove Thomsen, Henrik Madsen
      Pages 359-366
  7. Industrial Perspective on NMPC

    1. Kelvin Naidoo, John Guiver, Paul Turner, Mike Keenan, Michael Harmse
      Pages 383-398
    2. Rüdiger Franke, Jens Doppelhamer
      Pages 399-406
    3. Bjarne A. Foss, Tor S. Schei
      Pages 407-417
  8. NMPC and Process Control

    1. José M. Igreja, João M. Lemos, Rui Neves da Silva
      Pages 435-441
    2. Kenneth R. Muske, Amanda E. Witmer, Randy D. Weinstein
      Pages 443-453
    3. Peter Kühl, Moritz Diehl, Aleksandra Milewska, Eugeniusz Molga, Hans Georg Bock
      Pages 455-464
    4. Zoltan K. Nagy, Bernd Mahn, Rüdiger Franke, Frank Allgöwer
      Pages 465-472
    5. Renato Lepore, Alain Vande Wouwer, Marcel Remy, Philippe Bogaerts
      Pages 485-493
    6. D. Sarabia, C. de Prada, S. Cristea, R. Mazaeda, W. Colmenares
      Pages 495-502
    7. Anjali Deshpande, Sachin C. Patwardhan, Shankar Narasimhan
      Pages 513-521
  9. NMPC for Fast Systems

  10. Novel Applications of NMPC

    1. Xiao-Bing Hu, Wen-Hua Chen
      Pages 565-572
    2. Masayuki Fujita, Toshiyuki Murao, Yasunori Kawai, Yujiro Nakaso
      Pages 573-580
    3. Alessandro Casavola, Domenico Famularo, Giuseppe Franzè
      Pages 581-589

About this book


Thepastthree decadeshaveseenrapiddevelopmentin the areaofmodelpred- tive control with respect to both theoretical and application aspects. Over these 30 years, model predictive control for linear systems has been widely applied, especially in the area of process control. However, today’s applications often require driving the process over a wide region and close to the boundaries of - erability, while satisfying constraints and achieving near-optimal performance. Consequently, the application of linear control methods does not always lead to satisfactory performance, and here nonlinear methods must be employed. This is one of the reasons why nonlinear model predictive control (NMPC) has - joyed signi?cant attention over the past years,with a number of recent advances on both the theoretical and application frontier. Additionally, the widespread availability and steadily increasing power of today’s computers, as well as the development of specially tailored numerical solution methods for NMPC, bring thepracticalapplicabilityofNMPCwithinreachevenforveryfastsystems.This has led to a series of new, exciting developments, along with new challenges in the area of NMPC.


Model Predictive Control NMPC Obstacle Avoidance control feedback linear optimization path planning process control uncertainty

Editors and affiliations

  • Rolf Findeisen
    • 1
  • Frank Allgöwer
    • 1
  • Lorenz T. Biegler
    • 2
  1. 1.Institute for Systems Theory and Automatic ControlUniversity of StuttgartStuttgartGermany
  2. 2.Chemical Engineering DepartmentCarnegie Mellon UniversityPittsburghUSA

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag Berlin Heidelberg 2007
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering Engineering (R0)
  • Print ISBN 978-3-540-72698-2
  • Online ISBN 978-3-540-72699-9
  • Series Print ISSN 0170-8643
  • Buy this book on publisher's site