Ebook: Fully Tuned Radial Basis Function Neural Networks for Flight Control

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.

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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.

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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.
Content:
Front Matter....Pages i-xv
A Review of Nonlinear Adaptive Neural Control Schemes....Pages 1-24
Front Matter....Pages 25-28
Nonlinear System Identification Using Lyapunov-Based Fully Tuned RBFN....Pages 29-45
Real-Time Identification of Nonlinear Systems Using MRAN/EMRAN Algorithm....Pages 47-68
Indirect Adaptive Control Using Fully Tuned RBFN....Pages 69-80
Front Matter....Pages 81-83
Direct Adaptive Neuro Flight Controller Using Fully Tuned RBFN....Pages 85-94
Aircraft Flight Control Applications Using Direct Adaptive NFC....Pages 95-125
MRAN Neuro-Flight-Controller for Robust Aircraft Control....Pages 127-140
Conclusions and Future Work....Pages 141-144
Back Matter....Pages 145-158

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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.
Content:
Front Matter....Pages i-xv
A Review of Nonlinear Adaptive Neural Control Schemes....Pages 1-24
Front Matter....Pages 25-28
Nonlinear System Identification Using Lyapunov-Based Fully Tuned RBFN....Pages 29-45
Real-Time Identification of Nonlinear Systems Using MRAN/EMRAN Algorithm....Pages 47-68
Indirect Adaptive Control Using Fully Tuned RBFN....Pages 69-80
Front Matter....Pages 81-83
Direct Adaptive Neuro Flight Controller Using Fully Tuned RBFN....Pages 85-94
Aircraft Flight Control Applications Using Direct Adaptive NFC....Pages 95-125
MRAN Neuro-Flight-Controller for Robust Aircraft Control....Pages 127-140
Conclusions and Future Work....Pages 141-144
Back Matter....Pages 145-158
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