Discrete-Time Recurrent Neural Control: Analysis and Applications, 1st Edition (Hardback) book cover

Discrete-Time Recurrent Neural Control

Analysis and Applications, 1st Edition

By Edgar N. Sanchez

CRC Press

271 pages

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Hardback: 9781138550209
pub: 2018-09-04
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Description

The book presents recent advances in the theory of neural control for discrete-time nonlinear systems with multiple inputs and multiple outputs. The simulation results that appear in each chapter include rigorous mathematical analyses, based on the Lyapunov approach, to establish its properties. The book contains two sections: the first focuses on the analyses of control techniques; the second is dedicated to illustrating results of real-time applications. It also provides solutions for the output trajectory tracking problem of unknown nonlinear systems based on sliding modes and inverse optimal control scheme.

"This book on Discrete-time Recurrent Neural Control is unique in the literature, with new knowledge and information about the new technique of recurrent neural control especially for discrete-time systems.

The book is well organized and clearly presented. It will be welcome by a wide range of researchers in science and engineering, especially graduate students and junior researchers who want to learn the new notion of recurrent neural control. I believe it will have a good market.

It is an excellent book after all."

Guanrong Chen, City University of Hong Kong

"This book includes very relevant topics, about neural control. In these days, Artificial Neural Networks have been recovering their relevance and well-stablished importance, this due to its great capacity to process big amounts of data. Artificial Neural Networks development always is related to technological advancements; therefore, it is not a surprise that now we are being witnesses of this new era in Artificial Neural Networks, however most of the developments in this research area only focuses on applicability of the proposed schemes. However, Edgar N. Sanchez author of this book does not lose focus and include both important applications as well as a deep theoretical analysis of Artificial Neural Networks to control discrete-time nonlinear systems. It is important to remark that first, the considered Artificial Neural Networks are development in discrete-time this simplify its implementation in real-time; secondly, the proposed applications ranging from modelling of unknown discrete-time on linear systems to control electrical machines with an emphasize to renewable energy systems. However, its applications are not limited to these kind of systems, due to their theoretical foundation it can be applicable to a large class of nonlinear systems. All of these is supported by the solid research done by the author."

Alma Y. Alanis, University of Guadalajara, Mexico

"This book discusses in detail; how neural networks can be used for optimal as well as robust control design. Design of neural network controllers for real time applications such as induction motors, boost converters, inverted pendulum and doubly fed induction generators has also been carried out which gives the book an edge over other similar titles. This book will be an asset for the novice to the experienced ones."

Rajesh Joseph Abraham, Indian Institute of Space Science & Technology, Thiruvananthapuram, India

Reviews

"This book on Discrete-time Recurrent Neural Control is unique in the literature, with new knowledge and information about the new technique of recurrent neural control especially for discrete-time systems.

The book is well organized and clearly presented. It will be welcome by a wide range of researchers in science and engineering, especially graduate students and junior researchers who want to learn the new notion of recurrent neural control. I believe it will have a good market.

It is an excellent book after all."

Guanrong Chen, City University of Hong Kong

"This book includes very relevant topics, about neural control. In these days, Artificial Neural Networks have been recovering their relevance and well-stablished importance, this due to its great capacity to process big amounts of data. Artificial Neural Networks development always is related to technological advancements; therefore, it is not a surprise that now we are being witnesses of this new era in Artificial Neural Networks, however most of the developments in this research area only focuses on applicability of the proposed schemes. However, Edgar N. Sanchez author of this book does not lose focus and include both important applications as well as a deep theoretical analysis of Artificial Neural Networks to control discrete-time nonlinear systems. It is important to remark that first, the considered Artificial Neural Networks are development in discrete-time this simplify its implementation in real-time; secondly, the proposed applications ranging from modelling of unknown discrete-time on linear systems to control electrical machines with an emphasize to renewable energy systems. However, its applications are not limited to these kind of systems, due to their theoretical foundation it can be applicable to a large class of nonlinear systems. All of these is supported by the solid research done by the author."

Alma Y. Alanis, University of Guadalajara, Mexico

"This book discusses in detail; how neural networks can be used for optimal as well as robust control design. Design of neural network controllers for real time applications such as induction motors, boost converters, inverted pendulum and doubly fed induction generators has also been carried out which gives the book an edge over other similar titles. This book will be an asset for the novice to the experienced ones."

Rajesh Joseph Abraham, Indian Institute of Space Science & Technology, Thiruvananthapuram, India

Table of Contents

Section I Analyses

Chapter 1 Introduction

1.1 Preliminaries

1.2 Motivation

1.3 Objectives

1.4 Book Structure

1.5 Notation

1.6 Acronyms

Chapter 2 Mathematical Preliminaries

2.1 Optimal Control

2.2 Lyapunov Stability

2.3 Robust Stability Analysis

2.4 Passivity

2.5 Discrete-time High Order Neural Networks

2.6 The EKF Training Algorithm

2.7 Separation Principle for Discrete-time Nonlinear Systems

Chapter 3 Discrete Time Neural Block Control

3.1 Identification

3.2 Illustrative example

3.3 Neural Block Controller Design

3.4 Applications

3.5 Conclusions

Chapter 4 Neural Optimal Control

4.1 Inverse Optimal Control via CLF

4.2 Robust Inverse Optimal Control

4.3 Trajectory Tracking Inverse Optimal Control

4.4 CLF-based Inverse Optimal Control for a Class of Nonlinear Positive Systems

4.5 Speed-Gradient for the Inverse Optimal Control

4.6 Speed-Gradient Algorithm for Trajectory Tracking

4.7 Trajectory Tracking for Systems in Block-Control Form

4.8 Neural Inverse Optimal Control

4.9 Block-Control Form: A Nonlinear Systems Particular Class

4.10 Conclusions

Section II Real-time Applications

Chapter 5 Induction motors

5.1 Neural Identifier

5.2 Discrete-time super-twisting observer

5.3 Neural Sliding Modes Block Control

5.4 Neural Inverse Optimal Control

5.5 Real time Implementation

5.6 Prototype

5.7 Conclusions

Chapter 6 Doubly Fed Induction Generator

6.1 Neural Identifiers

6.2 Neural Sliding Modes Block Control

6.3 Neural Inverse Optimal Control

6.4 Implementation on a Wind Energy Testbed

6.5 Conclusions

Chapter 7 Conclusions

About the Author

Edgar N. Sanchez (M´85, SM´95) was born in 1949, in Sardinata, Colombia, South America. He obtained the BSEE, major in Power Systems, from Universidad Industrial de Santander (UIS), Bucaramanga, Colombia in 1971, the MSEE from CINVESTAVIPN (Advanced Studies and Research Center of the National Polytechnic Institute), major in Automatic Control, Mexico City, Mexico, in 1974 and the Docteur Ingenieur degree in Automatic Control from Institut Nationale Polytechnique de Grenoble, France in 1980. In 1971, 1972, 1975, and 1976, he worked for different Electrical Engineering consulting companies in Bogota, Colombia. In 1974, he was professor of Electrical Engineering Department of UIS, Colombia. From January 1981 to November 1990, he worked as a researcher at the Electrical Research Institute, Cuernavaca, Mexico. He was a professor of the graduate program in Electrical Engineering of the Universidad Autonoma de Nuevo Leon (UANL), Monterrey, Mexico, from December 1990 to December 1996. Since January 1997, he has been with CINVESTAV-IPN, Guadalajara Campus, Mexico, as a Professor of Electrical Engineering graduate programs. His research interest center in Neural Networks and Fuzzy Logic as applied to Automatic Control systems. He has been the advisor of 6 Ph. D. thesis and 33 M. Sc Thesis. He was granted an USA National Research Council Award as a research associate at NASA Langley Research Center, Hampton, Virginia, USA (January 1985 to March 1987). He is also member of the Mexican National Research System (promoted to highest rank, III, in 2005), the Mexican Academy of Science and the Mexican Academy of Engineering. He has published more than 100 technical papers in international journals and conferences and has served as reviewer for different international journals and conferences. He has also been member of many international conferences IPCs, both IEEE and IFAC.

About the Series

Automation and Control Engineering

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Subject Categories

BISAC Subject Codes/Headings:
TEC007000
TECHNOLOGY & ENGINEERING / Electrical
TEC008000
TECHNOLOGY & ENGINEERING / Electronics / General