Stable Adaptive Neural Network Control

This book is motivated by the need for systematic design approaches for stable adaptive control using approximation-based techniques.

Stable Adaptive Neural Network Control

Stable Adaptive Neural Network Control

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.

More Books:

Stable Adaptive Neural Network Control
Language: en
Pages: 282
Authors: S.S. Ge, C.C. Hang, T.H. Lee, Tao Zhang
Categories: Science
Type: BOOK - Published: 2013-03-09 - Publisher: Springer Science & Business Media

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
Adaptive Neural Network Control of Robotic Manipulators
Language: en
Pages: 381
Authors: Tong Heng Lee, Christopher John Harris
Categories: Science
Type: BOOK - Published: 1998 - Publisher: World Scientific

Introduction; Mathematical background; Dynamic modelling of robots; Structured network modelling of robots; Adaptive neural network control of robots; Neural network model reference adaptive control; Flexible joint robots; task space and force control; Bibliography; Computer simulation; Simulation software in C.
Advances in Neural Networks - ISNN 2007
Language: en
Pages: 1359
Authors: Derong Liu, Shumin Fei, Zeng-Guang Hou, Huaguang Zhang, Changyin Sun
Categories: Computers
Type: BOOK - Published: 2007-07-14 - Publisher: Springer

This book is part of a three volume set that constitutes the refereed proceedings of the 4th International Symposium on Neural Networks, ISNN 2007, held in Nanjing, China in June 2007. Coverage includes neural networks for control applications, robotics, data mining and feature extraction, chaos and synchronization, support vector machines,
Radial Basis Function (RBF) Neural Network Control for Mechanical Systems
Language: en
Pages: 365
Authors: Jinkun Liu
Categories: Technology & Engineering
Type: BOOK - Published: 2013-01-26 - Publisher: Springer Science & Business Media

Radial Basis Function (RBF) Neural Network Control for Mechanical Systems is motivated by the need for systematic design approaches to stable adaptive control system design using neural network approximation-based techniques. The main objectives of the book are to introduce the concrete design methods and MATLAB simulation of stable adaptive RBF
Stable Adaptive Control and Estimation for Nonlinear Systems
Language: en
Pages: 568
Authors: Jeffrey T. Spooner, Manfredi Maggiore, Raúl Ordóñez, Kevin M. Passino
Categories: Science
Type: BOOK - Published: 2004-04-07 - Publisher: John Wiley & Sons

Includes a solution manual for problems. Provides MATLAB code for examples and solutions. Deals with robust systems in both theory and practice.

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