Model-free stabilization by extremum seeking

With 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...

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Bibliographic Details
Main Author: Scheinker, Alexander.
Other Authors: Krstić, Miroslav.
Format: eBook
Language: English
Published: Cham : Springer, 2017.
Series: SpringerBriefs in electrical and computer engineering.
Subjects:
Variational principles.
Engineering.
Artificial intelligence.
System theory.
Calculus of variations.
Particle acceleration.
Automatic control.
elektronické knihy
electronic books
ISBN: 9783319507903
9783319507897
Physical Description: 1 online resource

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100 1 |a Scheinker, Alexander. 
245 1 0 |a Model-free stabilization by extremum seeking /  |c Alexander Scheinker, Miroslav Krstić. 
260 |a Cham :  |b Springer,  |c 2017. 
300 |a 1 online resource 
336 |a text  |b txt  |2 rdacontent 
337 |a počítač  |b c  |2 rdamedia 
338 |a online zdroj  |b cr  |2 rdacarrier 
490 1 |a SpringerBriefs in electrical and computer engineering,  |x 2191-8112 
505 0 |a Introduction -- 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. 
506 |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty 
520 |a With 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. 
504 |a Includes bibliographical references. 
590 |a SpringerLink  |b Springer Complete eBooks 
650 0 |a Variational principles. 
650 0 |a Engineering. 
650 0 |a Artificial intelligence. 
650 0 |a System theory. 
650 0 |a Calculus of variations. 
650 0 |a Particle acceleration. 
650 0 |a Automatic control. 
655 7 |a elektronické knihy  |7 fd186907  |2 czenas 
655 9 |a electronic books  |2 eczenas 
700 1 |a Krstić, Miroslav. 
776 0 8 |i Print version:  |z 3319507893  |z 9783319507897  |w (OCoLC)962896296 
830 0 |a SpringerBriefs in electrical and computer engineering. 
856 4 0 |u https://proxy.k.utb.cz/login?url=https://link.springer.com/10.1007/978-3-319-50790-3  |y Plný text 
992 |c NTK-SpringerENG 
999 |c 99659  |d 99659 
993 |x NEPOSILAT  |y EIZ 

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