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Explicit Nonlinear Model Predictive Control

Theory and Applications

  • Alexandra Grancharova
  • Tor Arne Johansen

Part of the Lecture Notes in Control and Information Sciences book series (LNCIS, volume 429)

Table of contents

  1. Front Matter
    Pages 1-12
  2. Alexandra Grancharova, Tor Arne Johansen
    Pages 1-37
  3. Alexandra Grancharova, Tor Arne Johansen
    Pages 39-69
  4. Alexandra Grancharova, Tor Arne Johansen
    Pages 71-85
  5. Alexandra Grancharova, Tor Arne Johansen
    Pages 87-110
  6. Alexandra Grancharova, Tor Arne Johansen
    Pages 111-125
  7. Alexandra Grancharova, Tor Arne Johansen
    Pages 127-156
  8. Alexandra Grancharova, Tor Arne Johansen
    Pages 157-186
  9. Alexandra Grancharova, Tor Arne Johansen
    Pages 187-207
  10. Alexandra Grancharova, Tor Arne Johansen
    Pages 209-231
  11. Back Matter
    Pages 0--1

About this book

Introduction

Nonlinear Model Predictive Control (NMPC) has become the accepted methodology to solve complex control problems related to process industries. The main motivation behind explicit NMPC is that an explicit state feedback law avoids the need for executing a numerical optimization algorithm in real time. The benefits of an explicit solution, in addition to the efficient on-line computations, include also verifiability of the implementation and the possibility to design embedded control systems with low software and hardware complexity.

This book considers the multi-parametric Nonlinear Programming (mp-NLP) approaches to explicit approximate NMPC of constrained nonlinear systems, developed by the authors, as well as their applications to various NMPC problem formulations and several case studies. The following types of nonlinear systems are considered, resulting in different NMPC problem formulations:

Ø  Nonlinear systems described by first-principles models and nonlinear systems described by black-box models;

Ø  Nonlinear systems with continuous control inputs and nonlinear systems with quantized control inputs;

Ø  Nonlinear systems without uncertainty and nonlinear systems with uncertainties (polyhedral description of uncertainty and stochastic description of uncertainty);

Ø  Nonlinear systems, consisting of interconnected nonlinear sub-systems.

The proposed mp-NLP approaches are illustrated with applications to several case studies, which are taken from diverse areas such as automotive mechatronics, compressor control, combustion plant control, reactor control, pH maintaining system control, cart and spring system control, and diving computers.

 

Keywords

Constrained Nonlinear Systems Interconnected Nonlinear Systems Nonlinear Model Predictive Control Parametric Programming Systems with Quantized Inputs

Authors and affiliations

  • Alexandra Grancharova
    • 1
  • Tor Arne Johansen
    • 2
  1. 1.and Robotics, Bulgarian Academy of SciencesInstitute of System EngineeringSofiaBulgaria
  2. 2.and Technology, Department of Engineering CyberneticsNorwegian University of ScienceTrondheimNorway

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-28780-0
  • Copyright Information Springer-Verlag Berlin Heidelberg 2012
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-28779-4
  • Online ISBN 978-3-642-28780-0
  • Series Print ISSN 0170-8643
  • Series Online ISSN 1610-7411
  • Buy this book on publisher's site
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