Automotive Model Predictive Control

Models, Methods and Applications

  • Luigi del Re
  • Frank Allgöwer
  • Luigi Glielmo
  • Carlos Guardiola
  • Ilya Kolmanovsky

Part of the Lecture Notes in Control and Information Sciences book series (LNCIS, volume 402)

Table of contents

  1. Front Matter
  2. Chances and Challenges in Automotive Predictive Control

    1. Luigi del Re, Peter Ortner, Daniel Alberer
      Pages 1-22
  3. Part I: Models

    1. Front Matter
      Pages 23-23
    2. Jean Arrègle, J. Javier López, Carlos Guardiola, Christelle Monin
      Pages 25-36
    3. Pierre Olivier Calendini, Stefan Breuer
      Pages 37-52
    4. Lars Eriksson, Johan Wahlström, Markus Klein
      Pages 53-71
    5. Markus Hirsch, Klaus Oppenauer, Luigi del Re
      Pages 73-87
  4. Part II: Methods

    1. Front Matter
      Pages 105-105
    2. Lalo Magni, Riccardo Scattolini
      Pages 107-117
    3. Bart Saerens, Moritz Diehl, Eric Van den Bulck
      Pages 119-138
    4. Mazen Alamir, André Murilo, Rachid Amari, Paolina Tona, Richard Fürhapter, Peter Ortner
      Pages 139-149
  5. Part III: Applications

    1. Front Matter
      Pages 151-151
    2. Per Tunestål, Magnus Lewander
      Pages 171-181
    3. Stefano Di Cairano, Diana Yanakiev, Alberto Bemporad, Ilya Kolmanovsky, Davor Hrovat
      Pages 183-194
    4. Paolo Falcone, Francesco Borrelli, Eric H. Tseng, Davor Hrovat
      Pages 195-210
    5. Greg Stewart, Francesco Borrelli, Jaroslav Pekar, David Germann, Daniel Pachner, Dejan Kihas
      Pages 211-230
    6. Giovanni Palmieri, Osvaldo Barbarisi, Stefano Scala, Luigi Glielmo
      Pages 231-255
    7. Jakob Ängeby, Matthias Huschenbett, Daniel Alberer
      Pages 257-272

About this book

Introduction

Automotive control has developed over the decades from an auxiliary te- nology to a key element without which the actual performances, emission, safety and consumption targets could not be met. Accordingly, automotive control has been increasing its authority and responsibility – at the price of complexity and di?cult tuning. The progressive evolution has been mainly ledby speci?capplicationsandshorttermtargets,withthe consequencethat automotive control is to a very large extent more heuristic than systematic. Product requirements are still increasing and new challenges are coming from potentially huge markets like India and China, and against this ba- ground there is wide consensus both in the industry and academia that the current state is not satisfactory. Model-based control could be an approach to improve performance while reducing development and tuning times and possibly costs. Model predictive control is a kind of model-based control design approach which has experienced a growing success since the middle of the 1980s for “slow” complex plants, in particular of the chemical and process industry. In the last decades, severaldevelopments haveallowedusing these methods also for “fast”systemsandthis hassupporteda growinginterestinitsusealsofor automotive applications, with several promising results reported. Still there is no consensus on whether model predictive control with its high requi- ments on model quality and on computational power is a sensible choice for automotive control.

Keywords

Constrained Control Control Control Design Control Engineering Crankshaft Models Model Predictive Control Nonlinear Control Nonlinear Model Predictive Control Optimization Simulation combustion

Editors and affiliations

  • Luigi del Re
    • 1
  • Frank Allgöwer
    • 2
  • Luigi Glielmo
    • 3
  • Carlos Guardiola
    • 4
  • Ilya Kolmanovsky
    • 5
  1. 1.Institute for Design and Control of Mechatronical SystemsJohannes Kepler University LinzLinz
  2. 2.Institute for Systems Theory and Automatic ControlUniversity of StuttgartStuttgart
  3. 3.Facoltà di IngegneriaUniversità del Sannio in BeneventoBenevento
  4. 4.Departamento de Máquinas y Motores TérmicosUniversidad Politécnica de Valencia (UPV)Valencia(Spain)
  5. 5.Ford Research and Adv. Engineering, Ford Motor CompanyTechnical Leader, Powertrain Control R&A

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-84996-071-7
  • Copyright Information Springer-Verlag London 2010
  • Publisher Name Springer, London
  • eBook Packages Engineering
  • Print ISBN 978-1-84996-070-0
  • Online ISBN 978-1-84996-071-7
  • Series Print ISSN 0170-8643
  • Series Online ISSN 1610-7411
  • About this book