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

About this book

Introduction

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.

Keywords

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 https://doi.org/10.1007/978-3-540-72699-9
  • Copyright Information Springer-Verlag Berlin Heidelberg 2007
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-540-72698-2
  • Online ISBN 978-3-540-72699-9
  • Series Print ISSN 0170-8643
  • About this book