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020 _a9781447167723
040 _aES-MaUEC
050 4 _aTJ217.5
_bO946 2016
100 1 _aOwens, David H.
_996525
_0Local
245 1 0 _aIterative Learning Control :
_bAn Optimization Paradigm
_cby David H Owens
250 _a1st ed.
260 _aLondon
_bSpringer London
_c2016
300 _a1 recurso en línea (XXVIII, 456 páginas) :
336 _aTexto
_btxt
_2rdacontent
337 _aelectrónico
_bc
_2rdamedia
338 _arecurso electrónico
_bcr
_2rdacarrier
490 0 _aAdvances in Industrial Control
_x1430-9491
505 0 _aIterative Learning Control: Background and Review. Mathematical and Linear Modelling Methodologies -- Norm Optimal Iterative Learning Control: An Optimal Control Perspective -- Predicting the Effects of Non-minimum-phase Zeros -- Predictive Norm Optimal Iterative Learning Control -- Other Applications of Norm Optimal Iterative Learning Control -- Successive Projection Algorithms -- Parameter Optimal Iterative Learning Control -- Robustness of Parameter Optimal Iterative Learning Control -- Multi-parameter Optimal Iterative Learning Control -- No Normal 0 false false false EN-GB X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-fareast-language:EN-US;} nlinear Iterative Learning Control and Optimization.
520 3 _aThis book develops a coherent theoretical approach to algorithm design for iterative learning control based on the use of optimization concepts. Concentrating initially on linear, discrete-time systems, the author gives the reader access to theories based on either signal or parameter optimization. Although the two approaches are shown to be related in a formal mathematical sense, the text presents them separately because their relevant algorithm design issues are distinct and give rise to different performance capabilities. Together with algorithm design, the text demonstrates that there are new algorithms that are capable of incorporating input and output constraints, enable the algorithm to reconfigure systematically in order to meet the requirements of different reference signals and also to support new algorithms for local convergence of nonlinear iterative control. Simulation and application studies are used to illustrate algorithm properties and performance in systems like gantry robots and other electromechanical and/or mechanical systems. Iterative Learning Control will interest academics and graduate students working in control who will find it a useful reference to the current status of a powerful and increasingly popular method of control. The depth of background theory and links to practical systems will be of use to engineers responsible for precision repetitive processes. Advances in Industrial Control aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.
710 2 _aSpringerLink (Online service)
_0Local
_9106996
942 _2lcc
_cLE
988 _aEBOOK, asignarmaterias , EBSPRINGER,
650 7 _aInteligencia artificial
_0comprobar BNE19900997218
_2embne
_9413115
650 0 7 _aSistemas, Teoría de
_0
_2embne
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856 4 0 _uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-1-4471-6772-3
_zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
901 _ai9781447167723
907 _a.b12936704
_b10-10-17
_c21-11-16
998 _am
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