LEADER 04238nam 22006855 450 001 9910157642003321 005 20220329221741.0 024 7 $a10.1007/978-3-319-50790-3 035 $a(CKB)3710000000985031 035 $a(DE-He213)978-3-319-50790-3 035 $a(MiAaPQ)EBC4773805 035 $a(PPN)197456316 035 $a(EXLCZ)993710000000985031 100 $a20161224d2017 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aModel-free stabilization by extremum seeking /$fby Alexander Scheinker, Miroslav Krsti? 205 $a1st ed. 2017. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017. 215 $a1 online resource (IX, 127 p. 46 illus., 33 illus. in color.) 225 1 $aSpringerBriefs in Control, Automation and Robotics,$x2192-6786 311 $a3-319-50789-3 311 $a3-319-50790-7 320 $aIncludes bibliographical references. 327 $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. 330 $aWith this brief, the authors present algorithms for model-free stabilization of unstable dynamic systems. An extremum-seeking algorithm assigns the role of a cost function to the dynamic system?s control Lyapunov function (clf) aiming at its minimization. The minimization of the clf drives the clf to zero and achieves asymptotic stabilization. This approach does not rely on, or require knowledge of, the system model. Instead, it employs periodic perturbation signals, along with the clf. The same effect is achieved as by using clf-based feedback laws that profit from modeling knowledge, but in a time-average sense. Rather than use integrals of the systems vector field, we employ Lie-bracket-based (i.e., derivative-based) averaging. The brief contains numerous examples and applications, including examples with unknown control directions and experiments with charged particle accelerators. It is intended for theoretical control engineers and mathematicians, and practitioners working in various industrial areas and in robotics. 410 0$aSpringerBriefs in Control, Automation and Robotics,$x2192-6786 606 $aAutomatic control 606 $aSystem theory 606 $aCalculus of variations 606 $aParticle acceleration 606 $aArtificial intelligence 606 $aControl and Systems Theory$3https://scigraph.springernature.com/ontologies/product-market-codes/T19010 606 $aSystems Theory, Control$3https://scigraph.springernature.com/ontologies/product-market-codes/M13070 606 $aCalculus of Variations and Optimal Control; Optimization$3https://scigraph.springernature.com/ontologies/product-market-codes/M26016 606 $aParticle Acceleration and Detection, Beam Physics$3https://scigraph.springernature.com/ontologies/product-market-codes/P23037 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 615 0$aAutomatic control. 615 0$aSystem theory. 615 0$aCalculus of variations. 615 0$aParticle acceleration. 615 0$aArtificial intelligence. 615 14$aControl and Systems Theory. 615 24$aSystems Theory, Control. 615 24$aCalculus of Variations and Optimal Control; Optimization. 615 24$aParticle Acceleration and Detection, Beam Physics. 615 24$aArtificial Intelligence. 676 $a620.104015118 700 $aScheinker$b Alexander$4aut$4http://id.loc.gov/vocabulary/relators/aut$0855654 702 $aKrsti?$b Miroslav$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910157642003321 996 $aModel-Free Stabilization by Extremum Seeking$91910349 997 $aUNINA