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| 001 | 978-1-4471-6772-3 | ||
| 003 | DE-He213 | ||
| 005 | 20220801214217.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 151031s2016 xxk| s |||| 0|eng d | ||
| 020 |
_a9781447167723 _9978-1-4471-6772-3 |
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| 024 | 7 |
_a10.1007/978-1-4471-6772-3 _2doi |
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| 050 | 4 | _aTJ212-225 | |
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_a003 _223 |
| 100 | 1 |
_aOwens, David H. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _936990 |
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| 245 | 1 | 0 |
_aIterative Learning Control _h[electronic resource] : _bAn Optimization Paradigm / _cby David H. Owens. |
| 250 | _a1st ed. 2016. | ||
| 264 | 1 |
_aLondon : _bSpringer London : _bImprint: Springer, _c2016. |
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| 300 |
_aXXVIII, 456 p. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
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| 490 | 1 |
_aAdvances in Industrial Control, _x2193-1577 |
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| 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 | _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. | ||
| 650 | 0 |
_aControl engineering. _931970 |
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| 650 | 0 |
_aSystem theory. _93409 |
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| 650 | 0 |
_aControl theory. _93950 |
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| 650 | 0 |
_aArtificial intelligence. _93407 |
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| 650 | 0 |
_aMachinery. _931894 |
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| 650 | 0 |
_aRobotics. _92393 |
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| 650 | 0 |
_aAutomation. _92392 |
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| 650 | 1 | 4 |
_aControl and Systems Theory. _931972 |
| 650 | 2 | 4 |
_aSystems Theory, Control . _931597 |
| 650 | 2 | 4 |
_aArtificial Intelligence. _93407 |
| 650 | 2 | 4 |
_aMachinery and Machine Elements. _931895 |
| 650 | 2 | 4 |
_aControl, Robotics, Automation. _931971 |
| 710 | 2 |
_aSpringerLink (Online service) _936991 |
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| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9781447167709 |
| 776 | 0 | 8 |
_iPrinted edition: _z9781447167716 |
| 776 | 0 | 8 |
_iPrinted edition: _z9781447169284 |
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_aAdvances in Industrial Control, _x2193-1577 _936992 |
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| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-1-4471-6772-3 |
| 912 | _aZDB-2-ENG | ||
| 912 | _aZDB-2-SXE | ||
| 942 | _cEBK | ||
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