Robust adaptive finite-time parameter estimation for linearly parameterized nonlinear systems

Jing Na; Muhammad Nasiruddin Mahyuddin; Guido Herrmann; Xuemei Ren

Robust adaptive finite-time parameter estimation for linearly parameterized nonlinear systems

Jing Na, Muhammad Nasiruddin Mahyuddin, Guido Herrmann, Xuemei Ren

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

57 Citations (Scopus)

Abstract

This paper studies a novel adaptive parameter estimation framework for linearly parameterized nonlinear systems. Appropriate parameter error information is derived by defining auxiliary filtered variables and used to drive the parameter adaptation, which guarantees exponential error convergence. The proposed method is further improved via a sliding mode approach to achieve finite-time (FT) error convergence. The case with bounded disturbances or noises is also studied. The parameter estimation is obtained without using the state derivatives and is independent of observer/predictor design. The online computation of the regressor matrix inverse can be avoided. Simulation examples are included to illustrate the effectiveness.

Original language	English
Title of host publication	Chinese Control Conference, CCC
Publisher	IEEE Computer Society
Pages	1735-1741
Number of pages	7
ISBN (Print)	9789881563835
Publication status	Published - 18 Oct 2013
Event	32nd Chinese Control Conference, CCC 2013 - Xi'an, United Kingdom Duration: 26 Jul 2013 → 28 Jul 2013

Conference

Conference	32nd Chinese Control Conference, CCC 2013
Country/Territory	United Kingdom
City	Xi'an
Period	26/07/13 → 28/07/13

Keywords

Adaptive system
Finite time convergence
Parameter estimation

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@inproceedings{c84e0a0bc81e4562ae85d395a2725dc0,

title = "Robust adaptive finite-time parameter estimation for linearly parameterized nonlinear systems",

abstract = "This paper studies a novel adaptive parameter estimation framework for linearly parameterized nonlinear systems. Appropriate parameter error information is derived by defining auxiliary filtered variables and used to drive the parameter adaptation, which guarantees exponential error convergence. The proposed method is further improved via a sliding mode approach to achieve finite-time (FT) error convergence. The case with bounded disturbances or noises is also studied. The parameter estimation is obtained without using the state derivatives and is independent of observer/predictor design. The online computation of the regressor matrix inverse can be avoided. Simulation examples are included to illustrate the effectiveness.",

keywords = "Adaptive system, Finite time convergence, Parameter estimation",

author = "Jing Na and Mahyuddin, {Muhammad Nasiruddin} and Guido Herrmann and Xuemei Ren",

year = "2013",

month = oct,

day = "18",

language = "English",

isbn = "9789881563835",

pages = "1735--1741",

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TY - GEN

T1 - Robust adaptive finite-time parameter estimation for linearly parameterized nonlinear systems

AU - Na, Jing

AU - Mahyuddin, Muhammad Nasiruddin

AU - Herrmann, Guido

AU - Ren, Xuemei

PY - 2013/10/18

Y1 - 2013/10/18

N2 - This paper studies a novel adaptive parameter estimation framework for linearly parameterized nonlinear systems. Appropriate parameter error information is derived by defining auxiliary filtered variables and used to drive the parameter adaptation, which guarantees exponential error convergence. The proposed method is further improved via a sliding mode approach to achieve finite-time (FT) error convergence. The case with bounded disturbances or noises is also studied. The parameter estimation is obtained without using the state derivatives and is independent of observer/predictor design. The online computation of the regressor matrix inverse can be avoided. Simulation examples are included to illustrate the effectiveness.

AB - This paper studies a novel adaptive parameter estimation framework for linearly parameterized nonlinear systems. Appropriate parameter error information is derived by defining auxiliary filtered variables and used to drive the parameter adaptation, which guarantees exponential error convergence. The proposed method is further improved via a sliding mode approach to achieve finite-time (FT) error convergence. The case with bounded disturbances or noises is also studied. The parameter estimation is obtained without using the state derivatives and is independent of observer/predictor design. The online computation of the regressor matrix inverse can be avoided. Simulation examples are included to illustrate the effectiveness.

KW - Adaptive system

KW - Finite time convergence

KW - Parameter estimation

UR - http://www.scopus.com/inward/record.url?scp=84890481682&partnerID=8YFLogxK

M3 - Conference Contribution (Conference Proceeding)

AN - SCOPUS:84890481682

SN - 9789881563835

SP - 1735

EP - 1741

BT - Chinese Control Conference, CCC

PB - IEEE Computer Society

T2 - 32nd Chinese Control Conference, CCC 2013

Y2 - 26 July 2013 through 28 July 2013

ER -

Robust adaptive finite-time parameter estimation for linearly parameterized nonlinear systems

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Keywords

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