Description: Discrete-Time High Order Neural Control by Edgar N. Sanchez, Alexander G. Loukianov, Alma Y. Alanís Neural networks have become a well-established methodology as exempli?ed by their applications to identi?cation and control of general nonlinear and complex systems; FORMAT Paperback LANGUAGE English CONDITION Brand New Publisher Description The objective of this work is to present recent advances in the theory of neural control for discrete-time nonlinear systems with multiple inputs and multiple outputs. The results that appear in each chapter include rigorous mathematical analyses, based on the Lyapunov approach, that guarantee its properties; in addition, for each chapter, simulation results are included to verify the successful performance of the corresponding proposed schemes. In order to complete the treatment of these schemes, the final chapter presents experimental results related to their application to a electric three phase induction motor, which show the applicability of such designs. The proposed schemes could be employed for different applications beyond the ones presented in this book. The book presents solutions for the output trajectory tracking problem of unknown nonlinear systems based on four schemes. For the first one, a direct design method is considered: the well known backstepping method, under the assumption of complete sate measurement; the second one considers an indirect method, solved with the block control and the sliding mode techniques, under the same assumption.For the third scheme, the backstepping technique is reconsidering including a neural observer, and finally the block control and the sliding mode techniques are used again too, with a neural observer. All the proposed schemes are developed in discrete-time. For both mentioned control methods as well as for the neural observer, the on-line training of the respective neural networks is performed by Kalman Filtering. Notes Presents recent advances in the theory of neural control for discrete-time nonlinear systems with multiple inputs and multiple outputs Back Cover The objective of this work is to present recent advances in the theory of neural control for discrete-time nonlinear systems with multiple inputs and multiple outputs. The results that appear in each chapter include rigorous mathematical analyses, based on the Lyapunov approach, in order to guarantee its properties; in addition, for each chapter, simulation results are included to verify the successful performance of the corresponding proposed schemes. In order to complete the treatment of these schemes, the book includes a chapter presenting experimental results related to their application to an electric three phase induction motor, which show the applicability of such designs. The proposed schemes could be employed for different applications beyond the ones presented in this book. The book presents solutions for the output trajectory tracking problem of unknown nonlinear systems based on four schemes. For the first one, a direct design method is considered: the well known backstepping method, under the assumption of complete state measurement; the second one considers an indirect method, solved with the block control and the sliding mode techniques, under the same assumption. For the third scheme, the backstepping technique is reconsidering including a neural observer, and finally the block control and the sliding mode techniques are used again too, with a neural observer. All the proposed schemes are developed in discrete-time. For both mentioned control methods as well as for the neural observer, the on-line training of the respective neural networks is performed by Kalman Filtering. Author Biography AM Editores is a publishing house founded in 1997 by the architects Fernando de Haro Lebrija and Omar Fuentes Elizondo, who have solid professional training that has allowed them to specialize not only in architectural and interior design, but also in publishing art books. Table of Contents Mathematical Preliminaries.- Discrete-Time Adaptive Neural Backstepping.- Discrete-Time Block Control.- Discrete-Time Neural Observers.- Discrete-Time Output Trajectory Tracking.- Real Time Implementation.- Conclusions and Future Work. Long Description Neural networks have become a well-established methodology as exemplied by their applications to identication and control of general nonlinear and complex systems; the use of high order neural networks for modeling and learning has recently increased. Usingneuralnetworks,controlalgorithmscanbedevelopedtoberobustto uncertainties and modeling errors. The most used NN structures are Feedf- ward networks and Recurrent networks. The latter type oers a better suited tool to model and control of nonlinear systems. There exist dierent training algorithms for neural networks, which, h- ever, normally encounter some technical problems such as local minima, slow learning, and high sensitivity to initial conditions, among others. As a viable alternative, new training algorithms, for example, those based on Kalman ?ltering, have been proposed. There already exists publications about trajectory tracking using neural networks; however, most of those works were developed for continuous-time systems. On the other hand, while extensive literature is available for linear discrete-timecontrolsystem,nonlineardiscrete-timecontroldesigntechniques have not been discussed to the same degree. Besides, discrete-time neural networks are better ?tted for real-time implementations. Feature Presents recent advances in the theory of neural control for discrete-time nonlinear systems with multiple inputs and multiple outputs Details ISBN3642096956 Author Alma Y. Alanís Publisher Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Series Studies in Computational Intelligence Year 2010 ISBN-10 3642096956 ISBN-13 9783642096952 Format Paperback Publication Date 2010-11-22 Imprint Springer-Verlag Berlin and Heidelberg GmbH & Co. K Place of Publication Berlin Country of Publication Germany DEWEY 006.32 Edition 1st Short Title DISCRETE-TIME HIGH ORDER NEURA Language English Media Book Series Number 112 Subtitle Trained with Kalman Filtering Pages 110 DOI 10.1007/978-3-540-78289-6 Edition Description Softcover reprint of hardcover 1st ed. 2008 Alternative 9783540782889 Audience Professional & Vocational Illustrations X, 110 p. 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ISBN-13: 9783642096952
Book Title: Discrete-Time High Order Neural Control
Number of Pages: 110 Pages
Publication Name: Discrete-Time High Order Neural Control: Trained with Kalman Filtering
Language: English
Publisher: Springer-Verlag Berlin and Heidelberg Gmbh & Co. Kg
Item Height: 235 mm
Subject: Engineering & Technology, Computer Science, Physics
Publication Year: 2010
Type: Textbook
Item Weight: 197 g
Subject Area: Material Science, Mechanical Engineering
Author: Alexander G. Loukianov, almay. Alanis, Edgar N. Sanchez
Item Width: 155 mm
Format: Paperback
