Distributed Model Predictive Control Made Easy

  • José M. Maestre
  • Rudy R. Negenborn

Table of contents

  1. Front Matter
    Pages i-xviii
  2. R. R. Negenborn, J. M. Maestre
    Pages 1-37
  3. From Small-Scale to Large-Scale: The Group of Autonomous Systems Perspective

    1. Front Matter
      Pages 39-39
    2. F. Valencia, J. D. López, J. A. Patiño, J. J. Espinosa
      Pages 41-56
    3. M. A. Müller, F. Allgöwer
      Pages 89-100
    4. R. Bourdais, J. Buisson, D. Dumur, H. Guéguen, P-D. Moroşan
      Pages 101-114
    5. B. Biegel, J. Stoustrup, P. Andersen
      Pages 179-192
    6. R. Hermans, M. Lazar, A. Jokić
      Pages 225-241
    7. A. Casavola, E. Garone, F. Tedesco
      Pages 259-274
    8. I. Prodan, F. Stoican, S. Olaru, C. Stoica, S.-I. Niculescu
      Pages 275-291
    9. A. Kozma, C. Savorgnan, M. Diehl
      Pages 327-340
    10. S. Roshany-Yamchi, R. R. Negenborn, A. A. Cornelio
      Pages 341-353
  4. From Large-Scale to Small-Scale: The Decomposed Monolithic System Perspective

    1. Front Matter
      Pages 355-355
    2. I. Jurado, D. E. Quevedo, K. H. Johansson, A. Ahlén
      Pages 357-373
    3. D. Axehill, A. Hansson
      Pages 375-392
    4. J. M. Maestre, F. J. Muros, F. Fele, D. Muñoz de la Peña, E. F. Camacho
      Pages 407-419
    5. G. Betti, M. Farina, R. Scattolini
      Pages 421-435
    6. A. Zafra-Cabeza, J. M. Maestre
      Pages 451-464
    7. J. M. Maestre, D. Muñoz de la Peña, E. F. Camacho
      Pages 465-477
    8. J. Liu, D. Muñoz de la Peña, P. D. Christofides
      Pages 479-494
    9. C. Ocampo-Martinez, V. Puig, J. M. Grosso, S. Montes-de-Oca
      Pages 495-515
    10. J. L. Nabais, R. R. Negenborn, R. B. Carmona-Benítez, L. F. Mendonça, M. A. Botto
      Pages 535-552
    11. G. Pannocchia, S. J. Wright, J. B. Rawlings
      Pages 553-568
    12. A. Ferramosca, D. Limon, A. H. González
      Pages 569-584

About this book


The rapid evolution of computer science, communication, and information technology has enabled the application of control techniques to systems beyond the possibilities of control theory just a decade ago. Critical infrastructures such as electricity, water, traffic and intermodal transport networks are now in the scope of control engineers. The sheer size of such large-scale systems requires the adoption of advanced distributed control approaches. Distributed model predictive control (MPC) is one of the promising control methodologies for control of such systems.


This book provides a state-of-the-art overview of distributed MPC approaches, while at the same time making clear directions of research that deserve more attention. The core and rationale of 35 approaches are carefully explained. Moreover, detailed step-by-step algorithmic descriptions of each approach are provided. These features make the book a comprehensive guide both for those seeking an introduction to distributed MPC as well as for those who want to gain a deeper insight in the wide range of distributed MPC techniques available.


Distributed, decentralized and hierarchical MPC Model Predictive Control

Editors and affiliations

  • José M. Maestre
    • 1
  • Rudy R. Negenborn
    • 2
  1. 1.Systems Engineering and Automation DeptUniversity of SevilleSevilleSpain
  2. 2.Dept of Marine & Transport TechnologyDelft University of TechnologyDelftThe Netherlands

Bibliographic information

  • DOI
  • Copyright Information Springer Science+Business Media Dordrecht 2014
  • Publisher Name Springer, Dordrecht
  • eBook Packages Engineering Engineering (R0)
  • Print ISBN 978-94-007-7005-8
  • Online ISBN 978-94-007-7006-5
  • Series Print ISSN 2213-8986
  • Series Online ISSN 2213-8994
  • Buy this book on publisher's site
Industry Sectors
Chemical Manufacturing
IT & Software
Materials & Steel
Oil, Gas & Geosciences