Adaptive sliding mode neural network control for nonlinear systems (Q1712283)

This book is organized as follows: The book is divided into 6 chapters: Chapter 1 contains basic concepts of the Lyapunov stability, advanced nonlinear systems control, and intelligent methodologies. Chapter 2: Nonlinear systems analysis approach. The chapter describes the exponential stability analysis of cellular neural networks based on linear matrix inequalities and robust Lyapunov stability analysis of the cellular neural networks based on linear matrix inequalities. Chapter 3: Classical nonlinear systems control. The chapter describes the sliding mode control approach and the backstepping control. Chapter 4: Advanced nonlinear systems controller design. Here the supertwisting synchronization control of chaotic system based on the U-model method, the supertwisting sliding mode control of nonlinear system-based on the U-model method and the sliding mode controller design for nonlinear systems with matching perturbations are investigated. Chapter 5: Intelligent methodology. Neural network identification and control for nonlinear dynamic systems and finite-time adaptive neural network control are the focus of the chapter. Chapter 6: Applications. It describes aircraft path planning based on neural networks and ADS-B for multilateration system using a BP neural network. References are given at the end of each chapter. Also, references for further reading are given. In addition, an index is provided. The book is well organized and presents the most important adaptive sliding mode neural network control method for nonlinear systems. Suitable for senior undergraduate and graduate students as well as practical engineers, scientists and researchers interested in adaptive sliding mode neural network control for nonlinear system.