Neural Networks Modelling and Control: Applications for Unknown Nonlinear Delayed Systems in Discrete-Time focuses on modelling 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. The designed neural controllers are applied in real-time first to a linear induction motor prototype and then to a networked robotic system. As well as considering the different neural control models and complications that are associated with them, this book alsoanalyzes potential applications, prototypes and future trends.
- In-depth analysis of neural control models and methodologies
- Comprehensive review of common problems in real-life neural network systems
- Analysis of potential applications, prototypes and future trends
Biomedical Engineers, researchers, and graduate students in neural engineering, neural mathematics and neural networks
1.1. Time-delay system
1.2. System model
1.3. Neural Identification
1.4. Neural state observer
1.5. Neural block control 1.5.1. Discrete-time Sliding mode control
1.5.2. Inverse optimal control
1.6. Problem Statement
1.8. Background information
1.9. Book Structure
2. Mathematical preliminaries
2.1. Time-delay systems
2.1.2. Time-delay system
2.1.3. Nonlinear discrete-time system with time-delays
2.2. Recurrent high order neural network 2.2.1. Discrete-time recurrent high order neural network
2.2.2. Extended Kalman Filter based training for recurrent high order neural networks
3. Recurrent high order neural network identification of nonlinear discrete-time unknown system with time-delays.
3.1. System identification
3.2. Neural Identification
3.3. Design of a neural identifier based on a recurrent high order neural network for a nonlinear discrete-time unknown system with
3.4. Simulation results of the recurrent high order neural network identifier
3.4.1. Van der Pol oscillator
3.4.2. Differential Robot
4. Neural identifier-control scheme for nonlinear discrete-time unknown system with time-delays
4.1. Neural identifier-control scheme, discrete-time sliding modes
4.1.1. Discrete-time sliding mode control
4.1.2. Real-time results of the neural identifier-control scheme using sliding mode control
18.104.22.168. Linear Induction motor with time-delays, test 1
22.214.171.124. Linear Induction motor with time-delays, test 2
126.96.36.199. Linear Induction motor with time-delays, test 3
4.2. Neural identifier-control scheme, inverse optimal control
4.2.1. inverse optimal control
4.2.2. Real-time results of the neural identifier-control scheme using inverse optimal control
188.8.131.52. Application to a differential robot
184.108.40.206.1. Differential robot, test 1
220.127.116.11.2. Differential robot, test 2
5. Recurrent high order neural network observer of nonlinear discrete-time unknown systems with time-delays.
5.1. Neural observer
5.2. Design of a full order neural observer based on a recurrent high order neural network for a nonlinear discrete-time unknown
system with time-delays.
5.2.1. Simulation results of the recurrent high order neural network full order observer
5.3. Design of a reduced order neural observer based on a recurrent high order neural network for a nonlinear discrete-time
unknown system with time-delays.
5.3.1. Simulation results of the recurrent high order neural network reduced order observer
6. Neural observer-control scheme for nonlinear discrete-time unknown system with time-delays
6.1. Design of a reduced order neural observer based on a recurrent high order neural network for a nonlinear discrete-time
unknown system with time-delays. 6.1.1. Simulation results of the neural observer-control
6.1.2. Real-time results of the neural observer-control
7. Concluding remarks and future trends
A. Artificial neural networks
a. Biological neural networks
i. Biological neuron
ii. Biological synapse
iii. Classification of neurons
b. Artificial neural networks
c. Activation functions
d. Artificial neural networks classification
i. Single-layer neural networks
ii. Multilayer neural networks
iii. Recurrent neural networks
B. Linear induction motor prototype
a. Linear induction motor
b. How does a linear induction motor work?
c. Linear induction motor model
d. Flux observer
e. Linear induction motor prototype
i. Electric drive by induction motor
ii. Linear induction motor prototype
iii. Prototype del robot differential
C. Differential robot prototype
a. All-terrain tracked robot
b. All-terrain tracked prototype
- No. of pages:
- © Academic Press 2020
- 1st January 2020
- Academic Press
- Paperback ISBN:
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.
Research Professor, CINVESTAV Guadalajara
Alma Y. Alanis, was born in Durango, Durango, Mexico, in 1980. She received the B. Sc. degree from Instituto Tecnologico de Durango (ITD), Durango Campus, Durango, Durango, in 2002, the M.Sc. and the Ph.D. degrees in electrical engineering from the Advanced Studies and Research Center of the National Polytechnic Institute (CINVESTAV-IPN), Guadalajara Campus, Mexico, in 2004 and 2007, respectively. Since 2008 she has been with University of Guadalajara, where she is currently a Chair Professor in the Department of Computer Science. She is also member of the Mexican National Research System (SNI-2) and member of the Mexican Academy of Sciences. She has published papers in recognized International Journals and Conferences, besides four International Books. She is a Senior Member of the IEEE and Subject and Associated Editor of the Journal of Franklin Institute (Elsevier) and Intelligent Automation and Soft Computing (Taylor and Francis), moreover she is currently serving on a number of IEEE and IFAC Conference Organizing Committees. In 2013, she receives the grant for women in science by L’Oreal-UNESCOAMC- CONACYT-CONALMEX. In 2015, she receives the Research Award Marcos Moshinsky. Since 2008 she is member for the Accredited Assessors record RCEACONACYT, evaluating a wide range of national research projects, besides she has belonged to important project evaluation committees of national and international research projects. Her research interest centers on neural control, backstepping control, block control, and their applications to electrical machines, power systems and robotics.
University of Guadalajara Guadalajara, Jalisco, Mexico
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.
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.
University of Guadalajara, Guadalajara, Jalisco, Mexico
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.
University of Guadalajara, Guadalajara, Jalisco, Mexico