Fully Tuned Radial Basis Function Neural Networks for Flight Control
By: by N. Sundararajan, P. Saratchandran, Yan Li
Contributors: Saratchandran, P.; Li, Yan
Series: The Springer International Series on Asian Studies in Computer and Information Science -- 12; Springer International Series on Asian Studies in Computer and Information Science -- 12.
Publisher: Springer US
Published: 2002
Place of publication: Boston, MA
Language: eng
Format: [electronic resource] /
Number of pages: 158
ISBN-13: 9781441949158; 9781475752861
Subjects: Mathematical optimization; Physics; Engineering; Artificial intelligence
Description: 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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