Adaptive stabilization by delay with biased measurements

Denis Efimov; Stanislav Aranovskiy; Emilia Fridman; Dimitry Sokolov; Jian Wang; Alexey A. Bobtsov

doi:10.1016/j.ifacol.2020.12.2234

Adaptive stabilization by delay with biased measurements

Denis Efimov, Stanislav Aranovskiy, Emilia Fridman, Dimitry Sokolov, Jian Wang, Alexey A. Bobtsov

School of Electrical Engineering

Tel Aviv University

Research output: Contribution to journal › Conference article › peer-review

Abstract

The problem of output robust adaptive stabilization for a class of Lipschitz nonlinear systems is studied under assumption that the measurements are available with a constant bias. The state reconstruction is avoided by using delayed values of the output in the feedback and adaptation laws. The control and adaptation gains can be selected as a solution of the proposed linear matrix inequalities (LMIs). The efficiency of the presented approach is demonstrated on a nonlinear pendulum through simulations.

Original language	English
Pages (from-to)	1684-1689
Number of pages	6
Journal	IFAC-PapersOnLine
Volume	53
DOIs	https://doi.org/10.1016/j.ifacol.2020.12.2234
State	Published - 2020
Event	21st IFAC World Congress 2020 - Berlin, Germany Duration: 12 Jul 2020 → 17 Jul 2020

All Science Journal Classification (ASJC) codes

Control and Systems Engineering

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10.1016/j.ifacol.2020.12.2234

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title = "Adaptive stabilization by delay with biased measurements",

abstract = "The problem of output robust adaptive stabilization for a class of Lipschitz nonlinear systems is studied under assumption that the measurements are available with a constant bias. The state reconstruction is avoided by using delayed values of the output in the feedback and adaptation laws. The control and adaptation gains can be selected as a solution of the proposed linear matrix inequalities (LMIs). The efficiency of the presented approach is demonstrated on a nonlinear pendulum through simulations.",

author = "Denis Efimov and Stanislav Aranovskiy and Emilia Fridman and Dimitry Sokolov and Jian Wang and Bobtsov, {Alexey A.}",

note = "Publisher Copyright: Copyright {\textcopyright} 2020 The Authors. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0); 21st IFAC World Congress 2020 ; Conference date: 12-07-2020 Through 17-07-2020",

year = "2020",

doi = "10.1016/j.ifacol.2020.12.2234",

language = "الإنجليزيّة",

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TY - JOUR

T1 - Adaptive stabilization by delay with biased measurements

AU - Efimov, Denis

AU - Aranovskiy, Stanislav

AU - Fridman, Emilia

AU - Sokolov, Dimitry

AU - Wang, Jian

AU - Bobtsov, Alexey A.

PY - 2020

Y1 - 2020

N2 - The problem of output robust adaptive stabilization for a class of Lipschitz nonlinear systems is studied under assumption that the measurements are available with a constant bias. The state reconstruction is avoided by using delayed values of the output in the feedback and adaptation laws. The control and adaptation gains can be selected as a solution of the proposed linear matrix inequalities (LMIs). The efficiency of the presented approach is demonstrated on a nonlinear pendulum through simulations.

AB - The problem of output robust adaptive stabilization for a class of Lipschitz nonlinear systems is studied under assumption that the measurements are available with a constant bias. The state reconstruction is avoided by using delayed values of the output in the feedback and adaptation laws. The control and adaptation gains can be selected as a solution of the proposed linear matrix inequalities (LMIs). The efficiency of the presented approach is demonstrated on a nonlinear pendulum through simulations.

UR - http://www.scopus.com/inward/record.url?scp=85107704538&partnerID=8YFLogxK

U2 - 10.1016/j.ifacol.2020.12.2234

DO - 10.1016/j.ifacol.2020.12.2234

M3 - مقالة من مؤنمر

SN - 2405-8963

VL - 53

SP - 1684

EP - 1689

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

T2 - 21st IFAC World Congress 2020

Y2 - 12 July 2020 through 17 July 2020

ER -

Adaptive stabilization by delay with biased measurements

Abstract

All Science Journal Classification (ASJC) codes

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