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Model Predictive Control in the Process Industry

  • Book
  • © 1995

Overview

Authors:
  1. Eduardo Fernandez-Camacho

    1. Escuela Superior de Ingenieros Industriales, Departamento de Ingeniería de Sistemas y Automática, Universidad de Sevilla, Sevilla, Spain

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  2. Carlos Bordons-Alba

    1. Escuela Superior de Ingenieros Industriales, Departamento de Ingeniería de Sistemas y Automática, Universidad de Sevilla, Sevilla, Spain

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    You can also search for this author in PubMed   Google Scholar

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

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Table of contents (8 chapters)

  1. Front Matter

    Pages i-xviii
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  2. Introduction to Model Based Predictive Control

    • Eduardo Fernandez-Camacho, Carlos Bordons-Alba
    Pages 1-8

  3. Model Based Predictive Controllers

    • Eduardo Fernandez-Camacho, Carlos Bordons-Alba
    Pages 9-37

  4. Simple Implementation of GPC for Industrial Processes

    • Eduardo Fernandez-Camacho, Carlos Bordons-Alba
    Pages 39-67

  5. Robustness Analysis in Precomputed GPC

    • Eduardo Fernandez-Camacho, Carlos Bordons-Alba
    Pages 69-103

  6. Multivariable GPC

    • Eduardo Fernandez-Camacho, Carlos Bordons-Alba
    Pages 105-128

  7. Constrained MPC

    • Eduardo Fernandez-Camacho, Carlos Bordons-Alba
    Pages 129-154

  8. Robust MPC

    • Eduardo Fernandez-Camacho, Camacho Carlos Bordons-Alba
    Pages 155-170

  9. Applications

    • Eduardo Fernandez-Camacho, Carlos Bordons-Alba
    Pages 171-195

  10. Back Matter

    Pages 197-239
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Keywords

About this book

Model Predictive Control is an important technique used in the process control industries. It has developed considerably in the last few years, because it is the most general way of posing the process control problem in the time domain. The Model Predictive Control formulation integrates optimal control, stochastic control, control of processes with dead time, multivariable control and future references. The finite control horizon makes it possible to handle constraints and non linear processes in general which are frequently found in industry. Focusing on implementation issues for Model Predictive Controllers in industry, it fills the gap between the empirical way practitioners use control algorithms and the sometimes abstractly formulated techniques developed by researchers. The text is firmly based on material from lectures given to senior undergraduate and graduate students and articles written by the authors.

Authors and Affiliations

  • Escuela Superior de Ingenieros Industriales, Departamento de Ingeniería de Sistemas y Automática, Universidad de Sevilla, Sevilla, Spain

    Eduardo Fernandez-Camacho, Carlos Bordons-Alba

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