This book reports on the latest findings concerning nonlinear control theory and applications. It presents novel work on several kinds of commonly encountered nonlinear time-delay systems, including t
Significant progress has been made on nonlinear control systems in the past two decades. However, many of the existing nonlinear control methods cannot be readily used to cope with communication and n
Colgren (aerospace engineering, University of Kansas) demonstrates the describing function method for determining the stability of non-linear systems, the optimal control synthesis method for achievin
Adaptive Sliding Mode Neural Network Control for Nonlinear Systems introduces nonlinear systems basic knowledge, analysis and control methods, and applications in various fields. It offers instructive
This book offers a comprehensive, easy-to-understand overview of receding-horizon control for nonlinear networks. It presents novel general strategies that can simultaneously handle general nonlinear
This is the first book to focus on solving cooperative control problems of multiple robot arms using different centralized or distributed neural network models, presenting methods and algorithms toget
Differential equations play a vital role in the fields of engineering and science. Problems in engineering and science can be modeled using ordinary or partial differential equations. Analytical solut
Provides a rigorous mathematical treatment for various proposed neural network and fuzzy system control schemes for control of uncertain nonlinear systems. Focus is on the application of neural networ
This booksummarizes Lyapunov design techniques for nonlinear systems andraises important issues concerning large-signal robustness and performance. The authors have been the first to addr
This book offers a novel approach to adaptive control and provides a sound theoretical background to designing robust adaptive control systems with guaranteed transient performance. It focuses on the
The book offers an insight on artificial neural networks for giving a robot a high level of autonomous tasks, such as navigation, object recognition, and clustering, with real-time implementations. It
In recent years, Higher Order Neural Networks (HONNs) have been widely adopted by researchers for applications in control signal generating, pattern recognition, nonlinear recognition, classification,
Fully Tuned Radial Basis Function Neural Networks for Flight Control presents the use of the Radial Basis Function (RBF) neural networks for adaptive control of nonlinear systems with emphasis on flig
This book reports on the latest findings in the study of Stochastic Neural Networks (SNN). The book collects the novel model of the disturbance driven by Levy process, the research method of M-matrix
This monograph is devoted to methods of reduction of nonlinear control systems to a simpler form: for example, decomposition into systems of lesser dimension. The approach centres on the immersion o
This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts, the book examines techniques fo
This concise and widely referenced monograph has served as a text for generations of advanced undergraduate math majors and graduate students. Prepared with an eye toward the needs of applied mathemat
