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Research Papers

Adaptive Estimation Using Multiagent Network Identifiers With Undirected and Directed Graph Topologies

[+] Author and Article Information
Teymur Sadikhov

School of Aerospace Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332
e-mail: tsadikhov@gatech.edu

Michael A. Demetriou

Professor
Department of Mechanical Engineering,
Worcester Polytechnic Institute,
Worcester, MA 01609
e-mail: mdemetri@wpi.edu

Wassim M. Haddad

Professor
School of Aerospace Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332
e-mail: wm.haddad@aerospace.gatech.edu

Tansel Yucelen

Assistant Professor
Mechanical and Aerospace Engineering,
Missouri University of Science and Technology,
Rolla, MO 65409
e-mail: yucelent@mst.edu

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received May 16, 2013; final manuscript received October 21, 2013; published online December 16, 2013. Assoc. Editor: Sergey Nersesov.

J. Dyn. Sys., Meas., Control 136(2), 021018 (Dec 16, 2013) (9 pages) Paper No: DS-13-1201; doi: 10.1115/1.4025802 History: Received May 16, 2013; Revised October 21, 2013

In this paper, we present an adaptive estimation framework predicated on multiagent network identifiers with undirected and directed graph topologies. Specifically, the system state and plant parameters are identified online using N agents implementing adaptive observers with an interagent communication architecture. The adaptive observer architecture includes an additive term which involves a penalty on the mismatch between the state and parameter estimates. The proposed architecture is shown to guarantee state and parameter estimate consensus. Furthermore, the proposed adaptive identifier architecture provides a measure of agreement of the state and parameter estimates that is independent of the network topology and guarantees that the deviation from the mean estimate for both the state and parameter estimates converge to zero. Finally, an illustrative numerical example is provided to demonstrate the efficacy of the proposed approach.

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Figures

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Fig. 1

System response and doublet input for Boeing 747

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Fig. 2

State error ∥ei(t)∥2 and ∥x∧ij(t)∥2 versus time for the proposed distributed adaptive observers given by Eqs. (28)–(30)

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Fig. 3

Estimate differences ∥A∧ij(t)∥F and ∥B∧ij(t)∥F versus time for the proposed distributed adaptive observers given by Eqs. (28)–(30)

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Fig. 4

Interagent communication graph topology

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Fig. 5

State error ∥ei(t)∥2 and ∥x∧ij(t)∥2 versus time for the proposed distributed adaptive observers given by Eqs. (44)–(46)

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Fig. 6

Estimate differences ∥A∧ij(t)∥F and ∥B∧ij(t)∥F versus time for the proposed distributed adaptive observers given by Eqs. (44)–(46)

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Fig. 7

State error ∥e(t)∥2 versus time for the centralized adaptive observer given by Eqs. (4)–(7).

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