Academic Press Neural Networks Modeling and Control: Applications for Unknown Nonlinear Delayed Systems in Discrete Time

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. Review An in-depth review of modeling and control of discrete-time unknown nonlinear delayed systems based on artificial neural networks About the Author 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. 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. 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 interna