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.
