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World Scientific Publishing Company Differential Neural Networks for Robust Nonlinear Control Book

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Description

Master advanced control theory with this technical text from World Scientific Publishing Company. This book explores continuous time dynamic neural networks and their practical application to solving fundamental problems in robust control theory. Readers will gain a deep understanding of identification, state space estimation using neuro-observers, and trajectory tracking. The text addresses complex scenarios where plants are unknown a priori, specifically focusing on systems that contain external perturbations and internal unmodelled dynamics. By studying the error stability analysis and the error bounds provided for various problems, engineers and researchers can better manage nonlinear control challenges. The book uses specific applications to illustrate how these suggested approaches work in real-world scenarios, making it a valuable resource for those studying AI, machine learning, and advanced control systems.

Key Features

Covers continuous time dynamic neural networks applied to robust control theory problems.

Provides detailed methods for identification and state space estimation using neuro-observers.

Explains trajectory tracking techniques for plants that are unknown a priori.

Addresses systems containing internal unmodelled dynamics and external perturbations.

Includes comprehensive error stability analysis and error bounds for different problems.

Uses practical applications to illustrate the effectiveness of the suggested approaches.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
01 October 2001
Listed Since
15 December 2006

Barcode

No barcode data available

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