國立高雄大學圖資館 |

登入

回首頁

Neural network-based adaptive control of uncertain nonlinear systems

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Neural network-based adaptive control of uncertain nonlinear systemsby Kasra Esfandiari, Farzaneh Abdollahi, Heidar A. Talebi.
作者:	Esfandiari, Kasra.
其他作者:	Abdollahi, Farzaneh.
出版者:	Cham :Springer International Publishing :2022.
面頁冊數:	xxiii, 163 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Adaptive control systems.
電子資源:	https://doi.org/10.1007/978-3-030-73136-6
ISBN:	9783030731366$q(electronic bk.)

Neural network-based adaptive control of uncertain nonlinear systems
Esfandiari, Kasra.

Neural network-based adaptive control of uncertain nonlinear systems[electronic resource] /by Kasra Esfandiari, Farzaneh Abdollahi, Heidar A. Talebi. - Cham :Springer International Publishing :2022. - xxiii, 163 p. :ill., digital ;24 cm.

Introduction -- Mathematical preliminaries -- NN-Based Adaptive Control of Affine Nonlinear Systems -- NN-Based Adaptive Control of Nonaffine Canonical Nonlinear -- Systems -- NN-Based Adaptive Control of Nonaffine Noncanonical Nonlinear -- NN-Based Adaptive Control of MIMO Nonaffine Noncanonical -- 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.

ISBN: 9783030731366$q(electronic bk.)

Standard No.: 10.1007/978-3-030-73136-6doiSubjects--Topical Terms:

182317
Adaptive control systems.

LC Class. No.: TJ217 / .E74 2022

Dewey Class. No.: 629.836

Neural network-based adaptive control of uncertain nonlinear systems
LDR:02837nmm a2200325 a 4500 001 617729
003 DE-He213
005 20210624191140.0
006 m d
007 cr nn 008maaau
008 221025s2022 sz s 0 eng d
020 $a 9783030731366$q(electronic bk.)
020 $a 9783030731359$q(paper)
024 7 $a 10.1007/978-3-030-73136-6 $2 doi
035 $a 978-3-030-73136-6
040 $a GP $c GP
041 0 $a eng
050 4 $a TJ217 $b .E74 2022
072 7 $a GPFC $2 bicssc
072 7 $a TEC009000 $2 bisacsh
072 7 $a GPFC $2 thema
082 0 4 $a 629.836 $2 23
090 $a TJ217 $b .E75 2022
100 1 $a Esfandiari, Kasra. $3 917325
245 1 0 $a Neural network-based adaptive control of uncertain nonlinear systems $h [electronic resource] / $c by Kasra Esfandiari, Farzaneh Abdollahi, Heidar A. Talebi.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2022.
300 $a xxiii, 163 p. : $b ill., digital ; $c 24 cm.
505 0 $a Introduction -- Mathematical preliminaries -- NN-Based Adaptive Control of Affine Nonlinear Systems -- NN-Based Adaptive Control of Nonaffine Canonical Nonlinear -- Systems -- NN-Based Adaptive Control of Nonaffine Noncanonical Nonlinear -- NN-Based Adaptive Control of MIMO Nonaffine Noncanonical -- Nonlinear Systems.
520 $a 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.
650 0 $a Adaptive control systems. $3 182317
650 0 $a Neural networks (Computer science) $3 181982
650 0 $a Nonlinear systems $x Automatic control. $3 516091
650 1 4 $a Complexity. $3 274400
650 2 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Mathematical Models of Cognitive Processes and Neural Networks. $3 567118
650 2 4 $a Power Electronics, Electrical Machines and Networks. $3 338660
700 1 $a Abdollahi, Farzaneh. $3 917326
700 1 $a Talebi, Heidar A. $3 485277
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-030-73136-6
950 $a Engineering (SpringerNature-11647)