Adaptive iterative learning control for nonlinear multi-agent systems consensus output tracking with actuator faults

Xu Jin

doi:10.1109/ACC.2016.7525089

Adaptive iterative learning control for nonlinear multi-agent systems consensus output tracking with actuator faults

Xu Jin

Mechanical and Aerospace Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Scopus citations

Abstract

In this work, we present a new distributed adaptive iterative learning control (AILC) scheme for a class of high-order nonlinear multi-agent systems (MAS) under alignment condition with both parametric and nonparametric system uncertainties, where the actuators may be faulty and the control input gain functions are not fully known. Nonparametric uncertainties such as norm-bounded nonlinear uncertainties can be effectively handled. Backstepping design with the composite energy function (CEF) structure is used in the discussion. Through rigorous analysis, we show that under this new AILC scheme, uniform convergence of agents output tracking error over the iteration domain is guaranteed. In the end, an illustrative example is presented to demonstrate the efficacy of the proposed AILC scheme.

Original language	English
Title of host publication	2016 American Control Conference, ACC 2016
Pages	1253-1258
Number of pages	6
ISBN (Electronic)	9781467386821
DOIs	https://doi.org/10.1109/ACC.2016.7525089
State	Published - Jul 28 2016
Event	2016 American Control Conference, ACC 2016 - Boston, United States Duration: Jul 6 2016 → Jul 8 2016

Publication series

Name	Proceedings of the American Control Conference
Volume	2016-July
ISSN (Print)	0743-1619

Conference

Conference	2016 American Control Conference, ACC 2016
Country/Territory	United States
City	Boston
Period	7/6/16 → 7/8/16

Bibliographical note

Publisher Copyright:
© 2016 American Automatic Control Council (AACC).

ASJC Scopus subject areas

Electrical and Electronic Engineering

Access to Document

10.1109/ACC.2016.7525089

Cite this

@inproceedings{3abf236819fb4bb8bf0c0dbe26711d6e,

title = "Adaptive iterative learning control for nonlinear multi-agent systems consensus output tracking with actuator faults",

abstract = "In this work, we present a new distributed adaptive iterative learning control (AILC) scheme for a class of high-order nonlinear multi-agent systems (MAS) under alignment condition with both parametric and nonparametric system uncertainties, where the actuators may be faulty and the control input gain functions are not fully known. Nonparametric uncertainties such as norm-bounded nonlinear uncertainties can be effectively handled. Backstepping design with the composite energy function (CEF) structure is used in the discussion. Through rigorous analysis, we show that under this new AILC scheme, uniform convergence of agents output tracking error over the iteration domain is guaranteed. In the end, an illustrative example is presented to demonstrate the efficacy of the proposed AILC scheme.",

author = "Xu Jin",

note = "Publisher Copyright: {\textcopyright} 2016 American Automatic Control Council (AACC).; 2016 American Control Conference, ACC 2016 ; Conference date: 06-07-2016 Through 08-07-2016",

year = "2016",

month = jul,

day = "28",

doi = "10.1109/ACC.2016.7525089",

language = "English",

series = "Proceedings of the American Control Conference",

pages = "1253--1258",

booktitle = "2016 American Control Conference, ACC 2016",

}

Jin, X 2016, Adaptive iterative learning control for nonlinear multi-agent systems consensus output tracking with actuator faults. in 2016 American Control Conference, ACC 2016., 7525089, Proceedings of the American Control Conference, vol. 2016-July, pp. 1253-1258, 2016 American Control Conference, ACC 2016, Boston, United States, 7/6/16. https://doi.org/10.1109/ACC.2016.7525089

Adaptive iterative learning control for nonlinear multi-agent systems consensus output tracking with actuator faults. / Jin, Xu.
2016 American Control Conference, ACC 2016. 2016. p. 1253-1258 7525089 (Proceedings of the American Control Conference; Vol. 2016-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Adaptive iterative learning control for nonlinear multi-agent systems consensus output tracking with actuator faults

AU - Jin, Xu

PY - 2016/7/28

Y1 - 2016/7/28

N2 - In this work, we present a new distributed adaptive iterative learning control (AILC) scheme for a class of high-order nonlinear multi-agent systems (MAS) under alignment condition with both parametric and nonparametric system uncertainties, where the actuators may be faulty and the control input gain functions are not fully known. Nonparametric uncertainties such as norm-bounded nonlinear uncertainties can be effectively handled. Backstepping design with the composite energy function (CEF) structure is used in the discussion. Through rigorous analysis, we show that under this new AILC scheme, uniform convergence of agents output tracking error over the iteration domain is guaranteed. In the end, an illustrative example is presented to demonstrate the efficacy of the proposed AILC scheme.

AB - In this work, we present a new distributed adaptive iterative learning control (AILC) scheme for a class of high-order nonlinear multi-agent systems (MAS) under alignment condition with both parametric and nonparametric system uncertainties, where the actuators may be faulty and the control input gain functions are not fully known. Nonparametric uncertainties such as norm-bounded nonlinear uncertainties can be effectively handled. Backstepping design with the composite energy function (CEF) structure is used in the discussion. Through rigorous analysis, we show that under this new AILC scheme, uniform convergence of agents output tracking error over the iteration domain is guaranteed. In the end, an illustrative example is presented to demonstrate the efficacy of the proposed AILC scheme.

UR - http://www.scopus.com/inward/record.url?scp=84992111327&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84992111327&partnerID=8YFLogxK

U2 - 10.1109/ACC.2016.7525089

DO - 10.1109/ACC.2016.7525089

M3 - Conference contribution

AN - SCOPUS:84992111327

T3 - Proceedings of the American Control Conference

SP - 1253

EP - 1258

BT - 2016 American Control Conference, ACC 2016

T2 - 2016 American Control Conference, ACC 2016

Y2 - 6 July 2016 through 8 July 2016

ER -

Adaptive iterative learning control for nonlinear multi-agent systems consensus output tracking with actuator faults

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this