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008			210618s2022 sz | s |||| 0|eng d
020			_a9783030731366
024	7		_a10.1007/978-3-030-73136-6 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTJ217 _b2022 EB
100	1		_aEsfandiari, Kasra. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	0	0	_aNeural Network-Based Adaptive Control of Uncertain Nonlinear Systems _cby Kasra Esfandiari, Farzaneh Abdollahi, Heidar A. Talebi.
250			_a1st edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (XXIII, 163 páginas) _b78 ilustraciones, 76 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
505	0		_aIntroduction -- 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			_aThe 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.
988			_aSpringer_Engineering_2022
650		7	_2embne _9405125 _aControl automático
776	0	8	_iPrinted edition: _z9783030731359
776	0	8	_iPrinted edition: _z9783030731373
776	0	8	_iPrinted edition: _z9783030731380
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-73136-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2023 _dz _eu _zSI