Neural Network-Based Adaptive Control of Uncertain Nonlinear Systems

The focus of this book is the application of artificial neural networks in uncertain dynamical systems. It explains how to use neural networks in concert with adaptive techniques for system identification, state estimation, and control problems. The authors begin with a brief historical overview of adaptive control, followed by a review of mathematical preliminaries. In the subsequent chapters, they present several neural network-based control schemes.
Each chapter starts with a concise introduction to the problem under study, and a neural network-based control strategy is designed for the simplest case scenario. After these designs are discussed, different practical limitations (i.e., saturation constraints and unavailability of all system states) are gradually added, and other control schemes are developed based on the primary scenario. Through these exercises, the authors present structures that not only provide mathematical tools for navigating control problems, but also supply solutions that are pertinent to real-life systems.
Strengthens understanding of neural networks for readers working on control theory, including various mathematical proofs and analyses;
Closely examines the use of neural networks for the control of uncertain dynamical systems;
Facilitates implementation of adaptive structures using updating rules originating in optimization algorithms;
Presents system identification, state estimation, and control schemes, applicable to a wide range of systems.