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Nonlinear Predictive Control Using Wiener Models

Computationally Efficient Approaches for Polynomial and Neural Structures

  • Book
  • © 2022

Overview

Authors:
  1. Maciej Ławryńczuk

    1. Institute of Control and Computation Engineering, Faculty of Electronics and Information Technology, Warsaw University of Technology, Warsaw, Poland

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  • Presents computationally efficient MPC algorithms for processes described by Wiener models
  • Provides computational efficiency of MPC as a key issue in this book
  • Shows approaches using on-line models or trajectory linearization

Part of the book series: Studies in Systems, Decision and Control (SSDC, volume 389)

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Table of contents (9 chapters)

  1. Front Matter

    Pages i-xxiii
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  2. Preliminaries

    1. Front Matter

      Pages 1-1
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    2. Introduction to Model Predictive Control

      • Maciej Ławryńczuk
      Pages 3-40

    3. Wiener Models

      • Maciej Ławryńczuk
      Pages 41-68

  3. Input-Output Approaches

    1. Front Matter

      Pages 69-69
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    2. MPC Algorithms Using Input-Output Wiener Models

      • Maciej Ławryńczuk
      Pages 71-141

    3. MPC of Input-Output Benchmark Wiener Processes

      • Maciej Ławryńczuk
      Pages 143-213

    4. Modelling and MPC of the Neutralisation Reactor Using Wiener Models

      • Maciej Ławryńczuk
      Pages 215-250

    5. Modelling and MPC of the Proton Exchange Membrane Fuel Cell Using Wiener Models

      • Maciej Ławryńczuk
      Pages 251-281

  4. State-Space Approaches

    1. Front Matter

      Pages 3-3
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    2. MPC Algorithms Using State-Space Wiener Models

      • Maciej Ławryńczuk
      Pages 285-308

    3. MPC of State-Space Benchmark Wiener Processes

      • Maciej Ławryńczuk
      Pages 309-336

    4. Conclusions

      • Maciej Ławryńczuk
      Pages 337-340

  5. Back Matter

    Pages 341-343
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Keywords

About this book

This book presents computationally efficient MPC solutions. The classical model predictive control (MPC) approach to control dynamical systems described by the Wiener model uses an inverse static block to cancel the influence of process nonlinearity. Unfortunately, the model's structure is limited, and it gives poor control quality in the case of an imperfect model and disturbances. An alternative is to use the computationally demanding MPC scheme with on-line nonlinear optimisation repeated at each sampling instant.


A linear approximation of the Wiener model or the predicted trajectory is found on-line. As a result, quadratic optimisation tasks are obtained. Furthermore, parameterisation using Laguerre functions is possible to reduce the number of decision variables. Simulation results for ten benchmark processes show that the discussed MPC algorithms lead to excellent control quality. For a neutralisation reactor and a fuel cell, essential advantages ofneural Wiener models are demonstrated.


Reviews

“The present book provides computationally efficient MPC (model predictive control) solutions as an alternative for the classical one, which has a limited structure, giving poor control quality in the case of an imperfect model and disturbances. The book is of real interest for all researchers working in control theory, optimization, engineering and economics.” (Savin Treanta, zbMATH 1510.93001, 2023)

Authors and Affiliations

  • Institute of Control and Computation Engineering, Faculty of Electronics and Information Technology, Warsaw University of Technology, Warsaw, Poland

    Maciej Ławryńczuk

Bibliographic Information

  • Book Title: Nonlinear Predictive Control Using Wiener Models

  • Book Subtitle: Computationally Efficient Approaches for Polynomial and Neural Structures

  • Authors: Maciej Ławryńczuk

  • Series Title: Studies in Systems, Decision and Control

  • DOI: https://doi.org/10.1007/978-3-030-83815-7

  • Publisher: Springer Cham

  • eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022

  • Hardcover ISBN: 978-3-030-83814-0Published: 22 September 2021

  • Softcover ISBN: 978-3-030-83817-1Published: 23 September 2022

  • eBook ISBN: 978-3-030-83815-7Published: 21 September 2021

  • Series ISSN: 2198-4182

  • Series E-ISSN: 2198-4190

  • Edition Number: 1

  • Number of Pages: XXIII, 343

  • Number of Illustrations: 46 b/w illustrations, 121 illustrations in colour

  • Topics: Control, Robotics, Mechatronics, Complexity

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