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Adaptive synchronization of unknown heterogeneous agents : An adaptive virtual model reference approach. / Baldi, Simone; Frasca, Paolo.

In: Journal of the Franklin Institute - Engineering and Applied Mathematics, Vol. 356, No. 2, 2019, p. 935-955.

Research output: Contribution to journalArticleScientificpeer-review

Harvard

Baldi, S & Frasca, P 2019, 'Adaptive synchronization of unknown heterogeneous agents: An adaptive virtual model reference approach' Journal of the Franklin Institute - Engineering and Applied Mathematics, vol. 356, no. 2, pp. 935-955. https://doi.org/10.1016/j.jfranklin.2018.01.022

APA

Baldi, S., & Frasca, P. (2019). Adaptive synchronization of unknown heterogeneous agents: An adaptive virtual model reference approach. Journal of the Franklin Institute - Engineering and Applied Mathematics, 356(2), 935-955. https://doi.org/10.1016/j.jfranklin.2018.01.022

Vancouver

Baldi S, Frasca P. Adaptive synchronization of unknown heterogeneous agents: An adaptive virtual model reference approach. Journal of the Franklin Institute - Engineering and Applied Mathematics. 2019;356(2):935-955. https://doi.org/10.1016/j.jfranklin.2018.01.022

Author

Baldi, Simone ; Frasca, Paolo. / Adaptive synchronization of unknown heterogeneous agents : An adaptive virtual model reference approach. In: Journal of the Franklin Institute - Engineering and Applied Mathematics. 2019 ; Vol. 356, No. 2. pp. 935-955.

BibTeX

@article{a403eafe8a6340918826642c1182d690,
title = "Adaptive synchronization of unknown heterogeneous agents: An adaptive virtual model reference approach",
abstract = "This work deals with state synchronization of heterogeneous linear agents with unknown dynamics. The problem is solved by formulating the synchronization problem as a special model reference adaptive control where each agent tries to converge to the model defined by its neighbors. For those agents that do not know the reference signal that drives the flock, a fictitious reference is estimated in place of the actual one: the estimation of such reference is distributed and requires measurements from neighbors. By using a matching condition assumption, which is imposed so that the agents can converge to the same behavior, the fictitious reference estimation leads to adaptive laws for the feedback and the coupling gains arising from distributed matching conditions. In addition, the coupling connection is not scalar as in most literature, but possibly vector-valued. The proposed approach is applicable to heterogeneous agents with arbitrarily large matched uncertainties. A Lyapunov-based approach is derived to show analytically asymptotic convergence of the synchronization error: robustification in the presence of bounded errors or unknown (constant) leader input is also discussed. Finally, a motivational example is presented in the context of Cooperative Adaptive Cruise Control and numerical examples are provided to demonstrate the effectiveness of the proposed method.",
author = "Simone Baldi and Paolo Frasca",
year = "2019",
doi = "10.1016/j.jfranklin.2018.01.022",
language = "English",
volume = "356",
pages = "935--955",
journal = "Journal of the Franklin Institute - Engineering and Applied Mathematics",
issn = "0016-0032",
publisher = "Elsevier",
number = "2",

}

RIS

TY - JOUR

T1 - Adaptive synchronization of unknown heterogeneous agents

T2 - Journal of the Franklin Institute - Engineering and Applied Mathematics

AU - Baldi, Simone

AU - Frasca, Paolo

PY - 2019

Y1 - 2019

N2 - This work deals with state synchronization of heterogeneous linear agents with unknown dynamics. The problem is solved by formulating the synchronization problem as a special model reference adaptive control where each agent tries to converge to the model defined by its neighbors. For those agents that do not know the reference signal that drives the flock, a fictitious reference is estimated in place of the actual one: the estimation of such reference is distributed and requires measurements from neighbors. By using a matching condition assumption, which is imposed so that the agents can converge to the same behavior, the fictitious reference estimation leads to adaptive laws for the feedback and the coupling gains arising from distributed matching conditions. In addition, the coupling connection is not scalar as in most literature, but possibly vector-valued. The proposed approach is applicable to heterogeneous agents with arbitrarily large matched uncertainties. A Lyapunov-based approach is derived to show analytically asymptotic convergence of the synchronization error: robustification in the presence of bounded errors or unknown (constant) leader input is also discussed. Finally, a motivational example is presented in the context of Cooperative Adaptive Cruise Control and numerical examples are provided to demonstrate the effectiveness of the proposed method.

AB - This work deals with state synchronization of heterogeneous linear agents with unknown dynamics. The problem is solved by formulating the synchronization problem as a special model reference adaptive control where each agent tries to converge to the model defined by its neighbors. For those agents that do not know the reference signal that drives the flock, a fictitious reference is estimated in place of the actual one: the estimation of such reference is distributed and requires measurements from neighbors. By using a matching condition assumption, which is imposed so that the agents can converge to the same behavior, the fictitious reference estimation leads to adaptive laws for the feedback and the coupling gains arising from distributed matching conditions. In addition, the coupling connection is not scalar as in most literature, but possibly vector-valued. The proposed approach is applicable to heterogeneous agents with arbitrarily large matched uncertainties. A Lyapunov-based approach is derived to show analytically asymptotic convergence of the synchronization error: robustification in the presence of bounded errors or unknown (constant) leader input is also discussed. Finally, a motivational example is presented in the context of Cooperative Adaptive Cruise Control and numerical examples are provided to demonstrate the effectiveness of the proposed method.

UR - http://resolver.tudelft.nl/uuid:a403eafe-8a63-4091-8826-642c1182d690

UR - http://www.scopus.com/inward/record.url?scp=85041924532&partnerID=8YFLogxK

U2 - 10.1016/j.jfranklin.2018.01.022

DO - 10.1016/j.jfranklin.2018.01.022

M3 - Article

VL - 356

SP - 935

EP - 955

JO - Journal of the Franklin Institute - Engineering and Applied Mathematics

JF - Journal of the Franklin Institute - Engineering and Applied Mathematics

SN - 0016-0032

IS - 2

ER -

ID: 44185218