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Neural Networks Modeling and Control

Applications for Unknown Nonlinear Delayed Systems in Discrete Time

  • 1st Edition - January 15, 2020
  • Latest edition
  • Authors: Jorge D. Rios, Alma Y Alanis, Nancy Arana-Daniel, Carlos Lopez-Franco
  • Editor: Edgar N. Sanchez
  • Language: English

Neural Networks Modelling and Control: Applications for Unknown Nonlinear Delayed Systems in Discrete Time focuses on modeling and control of discrete-time unknown nonlinear… Read more

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Description

Neural Networks Modelling and Control: Applications for Unknown Nonlinear Delayed Systems in Discrete Time focuses on modeling and control of discrete-time unknown nonlinear delayed systems under uncertainties based on Artificial Neural Networks. First, a Recurrent High Order Neural Network (RHONN) is used to identify discrete-time unknown nonlinear delayed systems under uncertainties, then a RHONN is used to design neural observers for the same class of systems. Therefore, both neural models are used to synthesize controllers for trajectory tracking based on two methodologies: sliding mode control and Inverse Optimal Neural Control.

As well as considering the different neural control models and complications that are associated with them, this book also analyzes potential applications, prototypes and future trends.

Key features

  • Provide in-depth analysis of neural control models and methodologies
  • Presents a comprehensive review of common problems in real-life neural network systems
  • Includes an analysis of potential applications, prototypes and future trends

Readership

Biomedical Engineers, researchers, and graduate students in neural engineering, neural mathematics and neural networks

Table of contents

1. Introduction1.1. Time-delay system1.2. System model1.3. Neural Identification1.4. Neural state observer1.5. Neural block control 1.5.1. Discrete-time Sliding mode control1.5.2. Inverse optimal control1.6. Problem Statement1.7. Objectives1.8. Background information1.9. Book Structure2. Mathematical preliminaries2.1. Time-delay systems2.1.1. Time-delay2.1.2. Time-delay system2.1.3. Nonlinear discrete-time system with time-delays2.2. Recurrent high order neural network 2.2.1. Discrete-time recurrent high order neural network2.2.2. Extended Kalman Filter based training for recurrent high order neural networks3. Recurrent high order neural network identification of nonlinear discrete-time unknown system with time-delays.3.1. System identification3.2. Neural Identification3.3. Design of a neural identifier based on a recurrent high order neural network for a nonlinear discrete-time unknown system withtime-delays.3.4. Simulation results of the recurrent high order neural network identifier3.4.1. Van der Pol oscillator3.4.2. Differential Robot4. Neural identifier-control scheme for nonlinear discrete-time unknown system with time-delays4.1. Neural identifier-control scheme, discrete-time sliding modes4.1.1. Discrete-time sliding mode control4.1.2. Real-time results of the neural identifier-control scheme using sliding mode control4.1.2.1. Linear Induction motor with time-delays, test 14.1.2.2. Linear Induction motor with time-delays, test 24.1.2.3. Linear Induction motor with time-delays, test 34.2. Neural identifier-control scheme, inverse optimal control4.2.1. inverse optimal control4.2.2. Real-time results of the neural identifier-control scheme using inverse optimal control4.2.2.1. Application to a differential robot4.2.2.1.1. Differential robot, test 14.2.2.1.2. Differential robot, test 25. Recurrent high order neural network observer of nonlinear discrete-time unknown systems with time-delays.5.1. Neural observer5.2. Design of a full order neural observer based on a recurrent high order neural network for a nonlinear discrete-time unknownsystem with time-delays.5.2.1. Simulation results of the recurrent high order neural network full order observer5.3. Design of a reduced order neural observer based on a recurrent high order neural network for a nonlinear discrete-timeunknown system with time-delays.5.3.1. Simulation results of the recurrent high order neural network reduced order observer6. Neural observer-control scheme for nonlinear discrete-time unknown system with time-delays6.1. Design of a reduced order neural observer based on a recurrent high order neural network for a nonlinear discrete-timeunknown system with time-delays. 6.1.1. Simulation results of the neural observer-control6.1.2. Real-time results of the neural observer-control7. Concluding remarks and future trendsAppendixA. Artificial neural networksa. Biological neural networksi. Biological neuronii. Biological synapseiii. Classification of neuronsb. Artificial neural networksc. Activation functionsd. Artificial neural networks classificationi. Single-layer neural networksii. Multilayer neural networksiii. Recurrent neural networksB. Linear induction motor prototypea. Linear induction motorb. How does a linear induction motor work?c. Linear induction motor modeld. Flux observere. Linear induction motor prototypei. Electric drive by induction motorii. Linear induction motor prototypeiii. Prototype del robot differentialC. Differential robot prototypea. All-terrain tracked robotb. All-terrain tracked prototype

Product details

  • Edition: 1
  • Latest edition
  • Published: January 15, 2020
  • Language: English

About the editor

ES

Edgar N. Sanchez

Edgar N. Sanchez was born in 1949, in Sardinata, Colombia, South America. He obtained his BSEE major in Power Systems from Universidad Industrial de Santander (UIS, Bucaramanga, Colombia) in 1971, his MSEE from CINVESTAV-IPN (Advanced Studies and Research Center of the National Polytechnic Institute), his major in Automatic Control (Mexico City, Mexico) in 1974, and his 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 a professor in the 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 at 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 in their graduate programs. His research interests are in neural networks and fuzzy logic as applied to automatic control systems. He has been the advisor of 21 Ph. D. theses and 40 M. Sc theses.

He was granted a 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 a 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 four books, more than 150 technical papers in international journals and conferences, and has served as a reviewer for different international journals and conferences. He has also been a member of many international conferences, both IEEE and IFAC.

Affiliations and expertise
Research Professor, CINVESTAV Guadalajara

About the authors

JR

Jorge D. Rios

Jorge D. Rios, was born in Guadalajara, Jalisco, Mexico, in 1985. He received the B.Sc. degree in Computer Engineering, in 2009, the M.Sc. and Ph. D. degrees in Electronics and Computer Engineering, in 2014 and 2017, respectively, from University of Guadalajara. He is in a Postdoctoral position at University of Guadalajara. His research interests center on neural control, nonlinear time-delay systems and their applications to electrical machines and robotics.
Affiliations and expertise
University of Guadalajara, Mexico

AY

Alma Y Alanis

Alma Y. Alanis received a Ph.D. degree in electrical engineering from the Advanced Studies and Research Center of the National Polytechnic Institute (CINVESTAV-IPN), Guadalajara Campus, Mexico, in 2007. Since 2008, she has been with the University of Guadalajara, where she is currently a Chair Professor in the Department of Computer Science. She is also a member of the Mexican National Research System (SNI-3) and the Mexican Academy of Sciences. She has published papers in recognized international journals and conferences, as well as eight international books. She is a Senior Member of the IEEE and a Subject Editor for the Journal of Franklin Institute (Elsevier), IEEE/ASME Transactions on Mechatronics, IEEE Access, IEEE Latin American Transactions, and Intelligent Automation & Soft Computing. In 2013, she received the grant for women in science by L'Oreal-UNESCO-AMC-CONACYT-CONALMEX. In 2015, she received the Marcos Moshinsky Research Award. Since 2008, she has been a member of the Accredited Assessors record RCEA-CONACYT, evaluating a wide range of national research projects, and has served on important national and international project evaluation committees. Her research interests center on neural control, backstepping control, block control, and their applications to electrical machines, power systems, and robotics.

Affiliations and expertise
Dean of Technologies for Cyber-Human Interaction Division (CUCEI), Universidad de Guadalajara, Mexico

NA

Nancy Arana-Daniel

Nancy Arana-Daniel received her B. Sc. Degree from the University of Guadalajara in 2000, and her M. Sc. And Ph.D. degrees in electric engineering with the special field in computer sicence from Research Center of the National Polytechnic Institute and Advanced Studies, CINVESTAV, in 2003 and 2007 respectively. She is currently a research fellow at the University of Guadalajara, in the Department of Computer Science Mxico, where she is working at the Laboratory of Intelligent Systems and the Research Center for Control Systems and Artificial Intelligence. She is IEEE Senior member and a member of National System of Researchers (SNI-1). She has published several papers in International Journals and Conferences and she has been technical manager of several projects that have been granted by the Nacional Council of Science and Technology (CONACYT). Also, se has collaborated in an international project granted by OPTREAT), She is Associated Editor of the Journal of Franklin Institute (Elsevier). Her research interests focus on applications of geometric algebra, geometric computing, machine learning, bio-inspired optimization, pattern recognition and robot navigation.
Affiliations and expertise
University of Guadalajara, Guadalajara, Jalisco, Mexico

CL

Carlos Lopez-Franco

Carlos Lpez-Franco received the Ph.D. degree in Computer Science in 2007 from the Center of Research and Advanced Studies, CINVESTAV, Mexico. He is currently a professor at the University of Guadalajara, Mexico, Computer Science Department, and member of the Intelligent Systems group. He is IEEE Senior member and a member of National System of Researchers) or SNI, level 1. His research interests include geometric algebra, computer vision, robotics and intelligent systems.
Affiliations and expertise
University of Guadalajara, Guadalajara, Jalisco, Mexico

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