Stable Adaptive Neural Network Control

S. S. Ge

ISBN 10: 0792375971 / ISBN 13: 9780792375975

Editore: Springer US Nov 2001, 2001

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This item is printed on demand - it takes 3-4 days longer - Neuware -Recent years have seen a rapid development of neural network control tech niques and their successful applications. Numerous simulation studies and actual industrial implementations show that artificial neural network is a good candidate for function approximation and control system design in solving the control problems of complex nonlinear systems in the presence of different kinds of uncertainties. Many control approaches/methods, reporting inventions and control applications within the fields of adaptive control, neural control and fuzzy systems, have been published in various books, journals and conference proceedings. In spite of these remarkable advances in neural control field, due to the complexity of nonlinear systems, the present research on adaptive neural control is still focused on the development of fundamental methodologies. From a theoretical viewpoint, there is, in general, lack of a firmly mathematical basis in stability, robustness, and performance analysis of neural network adaptive control systems. This book is motivated by the need for systematic design approaches for stable adaptive control using approximation-based techniques. The main objec tives of the book are to develop stable adaptive neural control strategies, and to perform transient performance analysis of the resulted neural control systems analytically. Other linear-in-the-parameter function approximators can replace the linear-in-the-parameter neural networks in the controllers presented in the book without any difficulty, which include polynomials, splines, fuzzy systems, wavelet networks, among others. Stability is one of the most important issues being concerned if an adaptive neural network controller is to be used in practical applications. 302 pp. Englisch.

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Dati bibliografici

• Titolo: Stable Adaptive Neural Network Control
• Editore: Springer US Nov 2001
• Data di pubblicazione: 2001
• Lingua: Inglese
• ISBN 10: 0792375971
• ISBN 13: 9780792375975
• Rilegatura: Buch
• Condizione: Neu
• Dimensioni: 241 L x 160 A x 21 P (millimetri) 241 millimetri di larghezza per 160 millimetri di altezza per 21 millimetri di profondità
• Peso: 620 grammi

Descrizione articolo

Riassunto

Recent years have seen a rapid development of neural network control tech­ niques and their successful applications. Numerous simulation studies and actual industrial implementations show that artificial neural network is a good candidate for function approximation and control system design in solving the control problems of complex nonlinear systems in the presence of different kinds of uncertainties. Many control approaches/methods, reporting inventions and control applications within the fields of adaptive control, neural control and fuzzy systems, have been published in various books, journals and conference proceedings. In spite of these remarkable advances in neural control field, due to the complexity of nonlinear systems, the present research on adaptive neural control is still focused on the development of fundamental methodologies. From a theoretical viewpoint, there is, in general, lack of a firmly mathematical basis in stability, robustness, and performance analysis of neural network adaptive control systems. This book is motivated by the need for systematic design approaches for stable adaptive control using approximation-based techniques. The main objec­ tives of the book are to develop stable adaptive neural control strategies, and to perform transient performance analysis of the resulted neural control systems analytically. Other linear-in-the-parameter function approximators can replace the linear-in-the-parameter neural networks in the controllers presented in the book without any difficulty, which include polynomials, splines, fuzzy systems, wavelet networks, among others. Stability is one of the most important issues being concerned if an adaptive neural network controller is to be used in practical applications.

Contenuti

1. Introduction. 2. Mathematical Preliminaries. 3. Neural Networks and Function Approximation. 4. SISO Nonlinear Systems. 5. ILF for Adaptive Control. 6. Non-affine Nonlinear Systems. 7. Triangular Nonlinear Systems. 8. Conclusion. References. Index.