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Economic Model Predictive Control

Theory, Formulations and Chemical Process Applications

  • Matthew Ellis
  • Jinfeng Liu
  • Panagiotis D. Christofides

Part of the Advances in Industrial Control book series (AIC)

Table of contents

  1. Front Matter
    Pages i-xxiv
  2. Matthew Ellis, Jinfeng Liu, Panagiotis D. Christofides
    Pages 1-19
  3. Matthew Ellis, Jinfeng Liu, Panagiotis D. Christofides
    Pages 21-55
  4. Matthew Ellis, Jinfeng Liu, Panagiotis D. Christofides
    Pages 57-73
  5. Matthew Ellis, Jinfeng Liu, Panagiotis D. Christofides
    Pages 75-133
  6. Matthew Ellis, Jinfeng Liu, Panagiotis D. Christofides
    Pages 135-170
  7. Matthew Ellis, Jinfeng Liu, Panagiotis D. Christofides
    Pages 171-232
  8. Matthew Ellis, Jinfeng Liu, Panagiotis D. Christofides
    Pages 233-289
  9. Back Matter
    Pages 291-292

About this book

Introduction

This book presents general methods for the design of economic model predictive control (EMPC) systems for broad classes of nonlinear systems that address key theoretical and practical considerations including recursive feasibility, closed-loop stability, closed-loop performance, and computational efficiency.  Specifically, the book proposes:

  • Lyapunov-based EMPC methods for nonlinear systems;
  •  two-tier EMPC architectures that are highly computationally efficient; and
  •  EMPC schemes handling explicitly uncertainty, time-varying cost functions, time-delays and multiple-time-scale dynamics.

The proposed methods employ a variety of tools ranging from nonlinear systems analysis, through Lyapunov-based control techniques to nonlinear dynamic optimization. The applicability and performance of the proposed methods are demonstrated through a number of chemical process examples.

The book presents state-of-the-art methods for the design of economic model predictive control systems for chemical processes. 

In addition to being mathematically rigorous, these methods accommodate key practical issues, for example, direct optimization of process economics, time-varying economic cost functions and computational efficiency. Numerous comments and remarks providing fundamental understanding of the merging of process economics and feedback control into a single framework are included. A control engineer can easily tailor the many detailed examples of industrial relevance given within the text to a specific application.

The authors present a rich collection of new research topics and references to significant recent work makingEconomic Model Predictive Control an important source of information and inspiration for academics and graduate students researching the area and for process engineers interested in applying its ideas.

Keywords

Model Predictive Control Process Economic Optimization Nonlinear Systems Computational Efficiency Time-varying Cost Function Time-delay Systems Multiple-time-scale Dynamics

Authors and affiliations

  • Matthew Ellis
    • 1
  • Jinfeng Liu
    • 2
  • Panagiotis D. Christofides
    • 3
  1. 1.Department of Chemical and Biomolecular EngineeringUniversity of California, Los AngelesLos AngelesUSA
  2. 2.Department of Chemical and Materials EngineeringUniversity of AlbertaEdmontonCanada
  3. 3.Department of Chemical and Biomolecular EngineeringUniversity of California, Los AngelesLos AngelesUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-41108-8
  • Copyright Information Springer International Publishing Switzerland 2017
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-41107-1
  • Online ISBN 978-3-319-41108-8
  • Series Print ISSN 1430-9491
  • Series Online ISSN 2193-1577
  • Buy this book on publisher's site
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