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Adaptive Sliding Mode Neural Network Control for Nonlinear Systems

2018-11-16 Technology & Engineering
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Release : 2018-11-16
Genre : Technology & Engineering
Kind : eBook
Book Rating : 322/5 ( reviews)

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Book Synopsis Adaptive Sliding Mode Neural Network Control for Nonlinear Systems by : Yang Li

Download or read book Adaptive Sliding Mode Neural Network Control for Nonlinear Systems written by Yang Li. This book was released on 2018-11-16. Available in PDF, EPUB and Kindle. Book excerpt: 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 examples and simulations, along with the source codes, and provides the basic architecture of control science and engineering. Introduces nonlinear systems' basic knowledge, analysis and control methods, along with applications in various fields Offers instructive examples and simulations, including source codes Provides the basic architecture of control science and engineering

Adaptive Control of Non-linear Systems Using Neural Networks

1990 Adaptive control systems
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Release : 1990
Genre : Adaptive control systems
Kind : eBook
Book Rating : /5 ( reviews)

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Book Synopsis Adaptive Control of Non-linear Systems Using Neural Networks by : Fu-Chuang Chen

Download or read book Adaptive Control of Non-linear Systems Using Neural Networks written by Fu-Chuang Chen. This book was released on 1990. Available in PDF, EPUB and Kindle. Book excerpt: Layered neural networks are used in the adaptive control of nonlinear discrete-time systems. The control algorithm is described and two convergence results are provided.

Stable Adaptive Control and Estimation for Nonlinear Systems

2004-04-07 Science
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Release : 2004-04-07
Genre : Science
Kind : eBook
Book Rating : 974/5 ( reviews)

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Book Synopsis Stable Adaptive Control and Estimation for Nonlinear Systems by : Jeffrey T. Spooner

Download or read book Stable Adaptive Control and Estimation for Nonlinear Systems written by Jeffrey T. Spooner. This book was released on 2004-04-07. Available in PDF, EPUB and Kindle. Book excerpt: Thema dieses Buches ist die Anwendung neuronaler Netze und Fuzzy-Logic-Methoden zur Identifikation und Steuerung nichtlinear-dynamischer Systeme. Dabei werden fortgeschrittene Konzepte der herkömmlichen Steuerungstheorie mit den intuitiven Eigenschaften intelligenter Systeme kombiniert, um praxisrelevante Steuerungsaufgaben zu lösen. Die Autoren bieten viel Hintergrundmaterial; ausgearbeitete Beispiele und Übungsaufgaben helfen Studenten und Praktikern beim Vertiefen des Stoffes. Lösungen zu den Aufgaben sowie MATLAB-Codebeispiele sind ebenfalls enthalten.

Adaptive Control of Nonlinear Systems Using Neural Networks

1995
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Release : 1995
Genre :
Kind : eBook
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Book Synopsis Adaptive Control of Nonlinear Systems Using Neural Networks by : Pravin Natarajan

Download or read book Adaptive Control of Nonlinear Systems Using Neural Networks written by Pravin Natarajan. This book was released on 1995. Available in PDF, EPUB and Kindle. Book excerpt:

Neural Network-Based Adaptive Control of Uncertain Nonlinear Systems

2021-06-18 Technology & Engineering
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Release : 2021-06-18
Genre : Technology & Engineering
Kind : eBook
Book Rating : 367/5 ( reviews)

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Book Synopsis Neural Network-Based Adaptive Control of Uncertain Nonlinear Systems by : Kasra Esfandiari

Download or read book Neural Network-Based Adaptive Control of Uncertain Nonlinear Systems written by Kasra Esfandiari. This book was released on 2021-06-18. Available in PDF, EPUB and Kindle. Book excerpt: 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.

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