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. - 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
Autorentext
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.
Inhalt
1. Introduction 2. Mathematical preliminaries 3. Recurrent high order neural network identification of nonlinear discrete-time unknown system with time-delays 4. Neural identifier-control scheme for nonlinear discrete-time unknown system with time-delays 5. Recurrent high order neural network observer of nonlinear discrete-time unknown systems with time-delays 6. Neural observer-control scheme for nonlinear discrete-time unknown system with time-delays 7. Concluding remarks and future trends
Appendix A. Artificial neural networks B. Linear induction motor prototype C. Differential robot prototype