000 | 03808nam a22006375i 4500 | ||
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001 | 978-3-319-50790-3 | ||
003 | DE-He213 | ||
005 | 20220801222428.0 | ||
007 | cr nn 008mamaa | ||
008 | 161224s2017 sz | s |||| 0|eng d | ||
020 |
_a9783319507903 _9978-3-319-50790-3 |
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024 | 7 |
_a10.1007/978-3-319-50790-3 _2doi |
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_a629.8312 _223 |
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_a003 _223 |
100 | 1 |
_aScheinker, Alexander. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _961441 |
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245 | 1 | 0 |
_aModel-Free Stabilization by Extremum Seeking _h[electronic resource] / _cby Alexander Scheinker, Miroslav Krstić. |
250 | _a1st ed. 2017. | ||
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2017. |
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300 |
_aIX, 127 p. 46 illus., 33 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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_acomputer _bc _2rdamedia |
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_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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490 | 1 |
_aSpringerBriefs in Control, Automation and Robotics, _x2192-6794 |
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505 | 0 | _aIntroduction -- Weak Limit Averaging for Studying the Dynamics of Extremum-Seeking-Stabilized Systems -- Minimization of Lyapunov Functions -- Control Affine Systems -- Non-C2 Extremum Seeking -- Bounded Extremum Seeking -- Extremum Seeking for Stabilization of Systems Not Affine in Control -- General Choice of Extremum-Seeking Dithers -- Application Study: Particle Accelerator Tuning. | |
520 | _aWith this brief, the authors present algorithms for model-free stabilization of unstable dynamic systems. An extremum-seeking algorithm assigns the role of a cost function to the dynamic system’s control Lyapunov function (clf) aiming at its minimization. The minimization of the clf drives the clf to zero and achieves asymptotic stabilization. This approach does not rely on, or require knowledge of, the system model. Instead, it employs periodic perturbation signals, along with the clf. The same effect is achieved as by using clf-based feedback laws that profit from modeling knowledge, but in a time-average sense. Rather than use integrals of the systems vector field, we employ Lie-bracket-based (i.e., derivative-based) averaging. The brief contains numerous examples and applications, including examples with unknown control directions and experiments with charged particle accelerators. It is intended for theoretical control engineers and mathematicians, and practitioners working in various industrial areas and in robotics. | ||
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 |
_aMathematical optimization. _94112 |
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650 | 0 |
_aCalculus of variations. _917382 |
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650 | 0 |
_aParticle accelerators. _919440 |
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650 | 0 |
_aArtificial intelligence. _93407 |
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650 | 1 | 4 |
_aControl and Systems Theory. _931972 |
650 | 2 | 4 |
_aSystems Theory, Control . _931597 |
650 | 2 | 4 |
_aCalculus of Variations and Optimization. _931596 |
650 | 2 | 4 |
_aAccelerator Physics. _938541 |
650 | 2 | 4 |
_aArtificial Intelligence. _93407 |
700 | 1 |
_aKrstić, Miroslav. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _961442 |
|
710 | 2 |
_aSpringerLink (Online service) _961443 |
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773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783319507897 |
776 | 0 | 8 |
_iPrinted edition: _z9783319507910 |
830 | 0 |
_aSpringerBriefs in Control, Automation and Robotics, _x2192-6794 _961444 |
|
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-319-50790-3 |
912 | _aZDB-2-ENG | ||
912 | _aZDB-2-SXE | ||
942 | _cEBK | ||
999 |
_c80758 _d80758 |