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© 2019

Model Predictive Control

Approaches Based on the Extended State Space Model and Extended Non-minimal State Space Model

Book

Table of contents

  1. Front Matter
    Pages i-xv
  2. Ridong Zhang, Anke Xue, Furong Gao
    Pages 1-14
  3. Basic Algorithms

    1. Front Matter
      Pages 15-15
    2. Ridong Zhang, Anke Xue, Furong Gao
      Pages 17-27
    3. Ridong Zhang, Anke Xue, Furong Gao
      Pages 29-35
    4. Ridong Zhang, Anke Xue, Furong Gao
      Pages 37-50
    5. Ridong Zhang, Anke Xue, Furong Gao
      Pages 59-63
    6. Ridong Zhang, Anke Xue, Furong Gao
      Pages 65-82
  4. System Performance Analysis, Optimization and Application

    1. Front Matter
      Pages 83-83
    2. Ridong Zhang, Anke Xue, Furong Gao
      Pages 85-92
    3. Ridong Zhang, Anke Xue, Furong Gao
      Pages 93-108
    4. Ridong Zhang, Anke Xue, Furong Gao
      Pages 109-125
    5. Ridong Zhang, Anke Xue, Furong Gao
      Pages 127-137

About this book

Introduction

This monograph introduces the authors’ work on model predictive control system design using extended state space and extended non-minimal state space approaches. It systematically describes model predictive control design for chemical processes, including the basic control algorithms, the extension to predictive functional control, constrained control, closed-loop system analysis, model predictive control optimization-based PID control, genetic algorithm optimization-based model predictive control, and industrial applications. Providing important insights, useful methods and practical algorithms that can be used in chemical process control and optimization, it offers a valuable resource for researchers, scientists and engineers in the field of process system engineering and control engineering. 

Keywords

Model Predictive Control Process Control Control Engineering State Space Models PID Control Extended state space models

Authors and affiliations

  1. 1.Institute of Information and ControlHangzhou Dianzi UniversityHangzhouChina
  2. 2.Key Lab for IOT and Information Fusion Technology of Zhejiang, Institute of Information and ControlHangzhou Dianzi UniversityHangzhouChina
  3. 3.Department of Chemical and Biomolecular EngineeringHong Kong University of Science and TechnologyHong KongChina

About the authors

Ridong Zhang received his Ph.D. in control science and engineering from Zhejiang University, Hangzhou, China, in 2007. From 2007 to 2015, he was a full professor at the Institute of Information and Control, Hangzhou Dianzi University, Hangzhou, and since 2015 he has been a visiting professor at the Chemical and Biomolecular Engineering Department, Hong Kong University of Science and Technology, Hong Kong. He has published more than 40 journal papers in the fields of process modeling and control. His research interests include process modeling, model predictive control and nonlinear systems.


Anke Xue received his Ph.D. in control science and engineering from Zhejiang University, Hangzhou, China, in 1997. He is currently a full professor at the Institute of Information and Control, Hangzhou Dianzi University, Hangzhou and the president of Hangzhou Dianzi University. He has published more than 50 journal papers in the fields of robust control and complex systems. His research interests include control theory and applications.


Furong Gao received his B.Eng. degree in automation from China University of Petroleum, China, in 1985 and his M.Eng. and Ph.D. degrees in chemical engineering from McGill University, Montreal, Canada in 1989 and 1993, respectively. He was a senior research engineer at Moldflow International Company Ltd. Since 1995, he has worked at Hong Kong University of Science and Technology, where he is currently a chair professor at the Department of Chemical and Biomolecular Engineering. His research interests include process monitoring and control, as well as polymer processing.


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