Data-driven methods for L2-gain estimation

QC 20210414
In this paper we present and discuss some data-driven methods for estimation of the L2-gain of dynamical systems. Partial results on convergence and statistical properties are provided. The methods are based on multiple experiments on the system. The main idea is to directly estimate the maximizing input signal by using iterative experiments on the true system. We study such a data-driven method based on a stochastic gradient method. We show that this method is very closely related to the so-called power iteration method based on the power method in numerical analysis. Furthermore, it is shown that this method is applicable for linear systems with noisy measurements. We will also study L2-gain estimation of Hammerstein systems. The stochastic gradient method and the power iteration method are evaluated and compared in simulation examples. Â© 2009 IFAC.

TEKNIK OCH TEKNOLOGIER Elektroteknik och elektronik Reglerteknik hsv//swe
ENGINEERING AND TECHNOLOGY Electrical Engineering, Electronic Engineering, Information Engineering Control Engineering hsv//eng
Infinity norm
Input signal design
L2-gain estimation
Power method
Small gain theorem
Gain estimation
Dynamical systems
Estimation
Experiments
Gradient methods
Linear systems
Numerical analysis
Stochastic systems
Numerical methods

Wahlberg, Bo,1959-KTH,Reglerteknik,ACCESS Linnaeus Centre,System Identification Group(Swepub:kth)u18c1tfa (author)
Hjalmarsson, HåkanKTH,Reglerteknik,ACCESS Linnaeus Centre(Swepub:kth)u10a8l40 (author)
Barkhagen, MathiasKTH,Reglerteknik(Swepub:kth)u1yetvyc (author)
KTHReglerteknik (creator_code:org_t)

By the author/editor: Barenthin, Märta; Wahlberg, Bo, 19 ...; Hjalmarsson, Håk ...; Barkhagen, Mathi ...

About the subject

ENGINEERING AND TECHNOLOGY: ENGINEERING AND ...; and Electrical Engin ...; and Control Engineer ...

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