000 03808nam a22006375i 4500
001 978-3-319-50790-3
003 DE-He213
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007 cr nn 008mamaa
008 161224s2017 sz | s |||| 0|eng d
020 _a9783319507903
_9978-3-319-50790-3
024 7 _a10.1007/978-3-319-50790-3
_2doi
050 4 _aTJ212-225
072 7 _aTJFM
_2bicssc
072 7 _aGPFC
_2bicssc
072 7 _aTEC004000
_2bisacsh
072 7 _aTJFM
_2thema
082 0 4 _a629.8312
_223
082 0 4 _a003
_223
100 1 _aScheinker, Alexander.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_961441
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.
300 _aIX, 127 p. 46 illus., 33 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringerBriefs in Control, Automation and Robotics,
_x2192-6794
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
650 0 _aSystem theory.
_93409
650 0 _aControl theory.
_93950
650 0 _aMathematical optimization.
_94112
650 0 _aCalculus of variations.
_917382
650 0 _aParticle accelerators.
_919440
650 0 _aArtificial intelligence.
_93407
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
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