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Fully Tuned Radial Basis Function Neural Networks for Flight Control

  • Book
  • © 2002

Overview

Authors:
  1. N. Sundararajan

    1. Nanyang Technological University, Singapore

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  2. P. Saratchandran

    1. Nanyang Technological University, Singapore

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    You can also search for this author in PubMed   Google Scholar

  3. Yan Li

    1. Nanyang Technological University, Singapore

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Table of contents (8 chapters)

  1. Front Matter

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

    1. A Review of Nonlinear Adaptive Neural Control Schemes

      • N. Sundararajan, P. Saratchandran, Yan Li
      Pages 1-24

  3. Nonlinear System Identification and Indirect Adaptive Control Schemes

    1. Front Matter

      Pages 25-28
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    2. Nonlinear System Identification Using Lyapunov-Based Fully Tuned RBFN

      • N. Sundararajan, P. Saratchandran, Yan Li
      Pages 29-45

    3. Real-Time Identification of Nonlinear Systems Using MRAN/EMRAN Algorithm

      • N. Sundararajan, P. Saratchandran, Yan Li
      Pages 47-68

    4. Indirect Adaptive Control Using Fully Tuned RBFN

      • N. Sundararajan, P. Saratchandran, Yan Li
      Pages 69-80

  4. Direct Adaptive Control Strategy and Fighter Aircraft Applications

    1. Front Matter

      Pages 81-83
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    2. Direct Adaptive Neuro Flight Controller Using Fully Tuned RBFN

      • N. Sundararajan, P. Saratchandran, Yan Li
      Pages 85-94

    3. Aircraft Flight Control Applications Using Direct Adaptive NFC

      • N. Sundararajan, P. Saratchandran, Yan Li
      Pages 95-125

    4. MRAN Neuro-Flight-Controller for Robust Aircraft Control

      • N. Sundararajan, P. Saratchandran, Yan Li
      Pages 127-140

    5. Conclusions and Future Work

      • N. Sundararajan, P. Saratchandran, Yan Li
      Pages 141-144

  5. Back Matter

    Pages 145-158
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Keywords

About this book

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.

Authors and Affiliations

  • Nanyang Technological University, Singapore

    N. Sundararajan, P. Saratchandran, Yan Li

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