Neural Network Based Adaptive Control Of Uncertain Nonlinear Systems
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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.
Nonlinear and Adaptive Control with Applications
Author: Alessandro Astolfi
language: en
Publisher: Springer Science & Business Media
Release Date: 2007-12-06
The authors here provide a detailed treatment of the design of robust adaptive controllers for nonlinear systems with uncertainties. They employ a new tool based on the ideas of system immersion and manifold invariance. New algorithms are delivered for the construction of robust asymptotically-stabilizing and adaptive control laws for nonlinear systems. The methods proposed lead to modular schemes that are easier to tune than their counterparts obtained from Lyapunov redesign.
Nonlinear Control of Engineering Systems
Author: Warren E. Dixon
language: en
Publisher: Springer Science & Business Media
Release Date: 2013-06-29
Recent advancements in Lyapunov-based design and analysis techniques have applications to a broad class of engineering systems, including mechanical, electrical, robotic, aerospace, and underactuated systems. This book provides a practical yet rigorous development of nonlinear, Lyapunov-based tools and their use in the solution of control-theoretic problems. Rich in motivating examples and new design techniques, the text balances theoretical foundations and real-world implementation. Features include: * Control designs for a broad class of engineering systems * Presentation of adaptive and learning control methods for uncertain nonlinear systems * Experimental testbed descriptions and results that guide the reader toward techniques for further research * Development of necessary mathematical background in each chapter; additional mathematical prerequisites contained in two appendices Intended for readers who have some knowledge of undergraduate systems theory, the book includes a wide range of applications making it suitable for an extensive audience. Graduate students and researchers in control systems, robotics, and applied mathematics, as well as professional engineers will appreciate the work's combination of theoretical underpinnings and current and emerging engineering applications.