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Fully Tuned Radial Basis Function Neural Networks for Flight Control

  • N. Sundararajan
  • P. Saratchandran
  • Yan Li

Table of contents

  1. Front Matter
    Pages i-xv
  2. A Review of Nonlinear Adaptive Neural Control Schemes

    1. N. Sundararajan, P. Saratchandran, Yan Li
      Pages 1-24
  3. Nonlinear System Identification and Indirect Adaptive Control Schemes

    1. Front Matter
      Pages 25-28
    2. N. Sundararajan, P. Saratchandran, Yan Li
      Pages 29-45
    3. N. Sundararajan, P. Saratchandran, Yan Li
      Pages 47-68
    4. N. Sundararajan, P. Saratchandran, Yan Li
      Pages 69-80
  4. Direct Adaptive Control Strategy and Fighter Aircraft Applications

    1. Front Matter
      Pages 81-83
    2. N. Sundararajan, P. Saratchandran, Yan Li
      Pages 85-94
    3. N. Sundararajan, P. Saratchandran, Yan Li
      Pages 95-125
    4. N. Sundararajan, P. Saratchandran, Yan Li
      Pages 127-140
    5. N. Sundararajan, P. Saratchandran, Yan Li
      Pages 141-144
  5. Back Matter
    Pages 145-158

About this book

Introduction

Fully Tuned Radial Basis Function Neural Networks for Flight Control presents the use of the Radial Basis Function (RBF) neural networks for adaptive control of nonlinear systems with emphasis on flight control applications. A Lyapunov synthesis approach is used to derive the tuning rules for the RBF controller parameters in order to guarantee the stability of the closed loop system. Unlike previous methods that tune only the weights of the RBF network, this book presents the derivation of the tuning law for tuning the centers, widths, and weights of the RBF network, and compares the results with existing algorithms. It also includes a detailed review of system identification, including indirect and direct adaptive control of nonlinear systems using neural networks.
Fully Tuned Radial Basis Function Neural Networks for Flight Control is an excellent resource for professionals using neural adaptive controllers for flight control applications.

Keywords

Adaptive control algorithms control neural networks nonlinear system

Authors and affiliations

  • N. Sundararajan
    • 1
  • P. Saratchandran
    • 1
  • Yan Li
    • 1
  1. 1.Nanyang Technological UniversitySingapore

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4757-5286-1
  • Copyright Information Springer-Verlag US 2002
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4419-4915-8
  • Online ISBN 978-1-4757-5286-1
  • Series Print ISSN 1566-0710
  • Buy this book on publisher's site