Dynamic Modeling, Predictive Control and Performance Monitoring

A Data-driven Subspace Approach

  • Authors
  • Biao Huang
  • Ramesh Kadali

Part of the Lecture Notes in Control and Information Sciences book series (LNCIS, volume 374)

Table of contents

  1. Front Matter
  2. Introduction

    1. Biao Huang, Ramesh Kadali
      Pages 1-5
  3. Part I Dynamic Modeling through Subspace Identification

    1. Front Matter
      Pages 7-7
    2. Biao Huang, Ramesh Kadali
      Pages 9-29
    3. Biao Huang, Ramesh Kadali
      Pages 31-53
    4. Biao Huang, Ramesh Kadali
      Pages 55-78
  4. Part II Predictive Control

    1. Front Matter
      Pages 99-99
    2. Biao Huang, Ramesh Kadali
      Pages 101-119
    3. Biao Huang, Ramesh Kadali
      Pages 121-141
  5. Part III Control Performance Monitoring

    1. Front Matter
      Pages 143-143
    2. Biao Huang, Ramesh Kadali
      Pages 145-155
    3. Biao Huang, Ramesh Kadali
      Pages 157-175
    4. Biao Huang, Ramesh Kadali
      Pages 213-227
  6. Back Matter

About this book

Introduction

A typical design procedure for model predictive control or control performance monitoring consists of: 1. identification of a parametric or nonparametric model; 2. derivation of the output predictor from the model; 3. design of the control law or calculation of performance indices according to the predictor.

Both design problems need an explicit model form and both require this three-step design procedure. Can this design procedure be simplified? Can an explicit model be avoided? With these questions in mind, the authors eliminate the first and second step of the above design procedure, a “data-driven” approach in the sense that no traditional parametric models are used; hence, the intermediate subspace matrices, which are obtained from the process data and otherwise identified as a first step in the subspace identification methods, are used directly for the designs. Without using an explicit model, the design procedure is simplified and the modelling error caused by parameterization is eliminated.

Keywords

Benchmarking Control Control Engineering Control Theory Model Predictive Control Monitoring Performance Monitoring Process Control Subspace System Identification model modeling

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-84800-233-3
  • Copyright Information Springer London 2008
  • Publisher Name Springer, London
  • eBook Packages Engineering
  • Print ISBN 978-1-84800-232-6
  • Online ISBN 978-1-84800-233-3
  • Series Print ISSN 0170-8643
  • Series Online ISSN 1610-7411
  • About this book