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E-grāmata: Discrete-Time Recurrent Neural Control: Analysis and Applications

(Unidad Guadalajara, Mexico.)
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Discrete-Time Recurrent Neural Control: Analysis and ApplicationsPalielināt
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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

Recenzijas

"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

Preface xv
Acknowledgments xvii
Authors xix
SECTION I Analyses
Chapter 1 Introduction
3(10)
1.1 Preliminaries
3(3)
1.2 Motivation
6(1)
1.3 Objectives
7(1)
1.4 Book Structure
7(1)
1.5 Notation
8(1)
1.6 Acronyms
9(4)
Chapter 2 Mathematical Preliminaries
13(22)
2.1 Optimal Control
13(3)
2.2 Lyapunov Stability
16(2)
2.3 Robust Stability Analysis
18(5)
2.3.1 Optimal Control for Disturbed Systems
23(1)
2.4 Passivity
23(2)
2.5 Discrete-time High Order Neural Networks
25(2)
2.6 The EKF Training Algorithm
27(2)
2.7 Separation Principle for Discrete-time Nonlinear Systems
29(6)
Chapter 3 Neural Block Control
35(28)
3.1 Identification
36(5)
3.2 Illustrative Example
41(5)
3.3 Neural Block Controller Design
46(5)
3.4 Applications
51(6)
3.4.1 Neural Network Identification
51(1)
3.4.2 Neural Block Controller Design
52(2)
3.4.3 Reduced Order Nonlinear Observer
54(2)
3.4.4 Simulation Results
56(1)
3.5 Conclusions
57(6)
Chapter 4 Neural Optimal Control
63(92)
4.1 Inverse Optimal Control via CLF
63(12)
4.1.1 Example
70(2)
4.1.2 Inverse Optimal Control for Linear Systems
72(3)
4.2 Robust Inverse Optimal Control
75(11)
4.3 Trajectory Tracking Inverse Optimal Control
86(11)
4.3.1 Application to the Boost Converter
92(2)
4.3.1.1 Boost Converter Model
94(1)
4.3.1.2 Control Synthesis
95(1)
4.3.1.3 Simulation Results
96(1)
4.4 CLF-based Inverse Optimal Control for a Class of Nonlinear Positive Systems
97(5)
4.5 Speed-gradient for the Inverse Optimal Control
102(17)
4.5.1 Speed-gradient Algorithm
103(5)
4.5.2 Summary of the Proposed SG Algorithm to Calculate Parameter pk
108(1)
4.5.3 SG Inverse Optimal Control
108(4)
4.5.3.1 Example
112(3)
4.5.4 Application to the Inverted Pendulum on a Cart
115(1)
4.5.4.1 Simulation Results
116(3)
4.6 Speed-gradient Algorithm for Trajectory Tracking
119(5)
4.6.1 Example
123(1)
4.7 Trajectory Tracking for Systems in Block-control Form
124(5)
4.7.1 Example
128(1)
4.8 Neural Inverse Optimal Control
129(16)
4.8.1 Stabilization
131(1)
4.8.1.1 Example
131(3)
4.8.2 Trajectory Tracking
134(2)
4.8.2.1 Application to a Synchronous Generator
136(8)
4.8.2.2 Comparison
144(1)
4.9 Block-control Form: A Nonlinear Systems Particular Class
145(3)
4.9.1 Block Transformation
145(3)
4.9.2 Block Inverse Optimal Control
148(1)
4.10 Conclusions
148(7)
SECTION II Real-Time Applications
Chapter 5 Induction Motors
155(28)
5.1 Neural Identifier
156(1)
5.2 Discrete-time Super-twisting Observer
157(1)
5.3 Neural Sliding Modes Block Control
158(2)
5.4 Neural Inverse Optimal Control
160(1)
5.5 Implementation
161(1)
5.6 Prototype
162(18)
5.6.1 RCP System
162(4)
5.6.2 Power Electronics
166(2)
5.6.3 Signal Conditioning for ADC
168(1)
5.6.4 Real-time Controller Implementation
168(1)
5.6.4.1 Induction Motor Inputs and Outputs
169(6)
5.6.4.2 Flux Observer
175(1)
5.6.4.3 Neural Identifier
175(1)
5.6.4.4 Serial Communication Interface
175(1)
5.6.5 Neural Sliding Mode Real-time Results
175(1)
5.6.6 Neural Inverse Optimal Control Real-time Results
176(4)
5.7 Conclusions
180(3)
Chapter 6 Doubly Fed Induction Generator
183(58)
6.1 Neural Identifiers
184(2)
6.1.1 DFIG Neural Identifier
184(1)
6.1.2 DC Link Neural Identifier
185(1)
6.2 Neural Sliding Mode Block Control
186(17)
6.2.1 DFIG Controller
186(5)
6.2.1.1 Simulation Results
191(5)
6.2.2 DC Link Controller
196(3)
6.2.2.1 Simulation Results
199(4)
6.3 Neural Inverse Optimal Control
203(12)
6.3.1 DFIG Controller
203(4)
6.3.1.1 Simulation Results
207(3)
6.3.2 DC Link Controller
210(3)
6.3.2.1 Simulation Results
213(2)
6.4 Implementation on a Wind Energy Testbed
215(22)
6.4.1 Real-time Controller Programing
216(4)
6.4.2 Doubly Fed Induction Generator Prototype
220(7)
6.4.3 Sliding Mode Real-time Results
227(3)
6.4.4 Neural Sliding Mode Real-time Results
230(3)
6.4.5 Neural Inverse Optimal Control Real-time Results
233(4)
6.5 Conclusions
237(4)
Chapter 7 Conclusions
241(26)
A DFIG and DC Link Mathematical Model
243(24)
A.1 DFIG Mathematical Model
243(6)
A.1.1 Variables Transformation Referred to a Reference Frame Fixed in the Rotor
249(3)
A.1.2 Torque Equation in Arbitrary Reference-frame Variables
252(1)
A.1.3 Per-unit Conversion
253(4)
A.1.4 DFIG State Variables Model
257(3)
A.2 DC Link Mathematical Model
260(7)
Index 267
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.
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