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Research Papers

Mitigating the Effects of Sensor Uncertainties in Networked Multi-Agent Systems

[+] Author and Article Information
Ehsan Arabi

Department of Mechanical Engineering,
University of South Florida,
Tampa, FL 33620
e-mail: arabi@lacis.team

Tansel Yucelen

Department of Mechanical Engineering,
University of South Florida,
Tampa, FL 33620
e-mail: yucelen@lacis.team

Wassim M. Haddad

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

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received October 27, 2015; final manuscript received October 15, 2016; published online February 6, 2017. Assoc. Editor: Mazen Farhood.

J. Dyn. Sys., Meas., Control 139(4), 041003 (Feb 06, 2017) (11 pages) Paper No: DS-15-1538; doi: 10.1115/1.4035092 History: Received October 27, 2015; Revised October 15, 2016

Networked multi-agent systems consist of interacting agents that locally exchange information, energy, or matter. Since these systems do not in general have a centralized architecture to monitor the activity of each agent, resilient distributed control system design for networked multi-agent systems is essential in providing high system performance, reliability, and operation in the presence of system uncertainties. An important class of such system uncertainties that can significantly deteriorate the achievable closed-loop system performance is sensor uncertainties, which can arise due to low sensor quality, sensor failure, sensor bias, or detrimental environmental conditions. This paper presents a novel distributed adaptive control architecture for networked multi-agent systems with undirected communication graph topologies to mitigate the effect of sensor uncertainties. Specifically, we consider agents having identical high-order, linear dynamics with agent interactions corrupted by unknown exogenous disturbances. We show that the proposed adaptive control architecture guarantees asymptotic stability of the closed-loop dynamical system when the exogenous disturbances are time-invariant and uniform ultimate boundedness when the exogenous disturbances are time-varying. Two numerical examples are provided to illustrate the efficacy of the proposed distributed adaptive control architecture.

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Figures

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Fig. 1

A networked multi-agent system with agents lying on an agent layer and their local controllers lying on a control layer

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Fig. 5

System performance for a group of agents in Example 1 with the proposed local controller given by Eq. (10) and the local corrective signal given by Eq. (11) when the compromised state measurement is available for feedback

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Fig. 6

Time evolution of e(t), t ≥ 0, in Example 1 with the proposed local controller given by Eq. (10) and the local corrective signal given by Eq. (11) when the compromised state measurement is available for feedback

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Fig. 7

Time evolution of δ̃(t),t≥0, in Example 1 with the proposed local controller given by Eq. (10) and the local corrective signal given by Eq. (11) when the compromised state measurement is available for feedback

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Fig. 4

System performance for a group of agents in Example 1 with the local controller given by Eq. (7) (i.e., vi(t)≡0, i=1,…,4) when the compromised state measurement is available for feedback

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Fig. 10

System performance for a group of agents in Example 2 with the local controller given by Eq. (7) (i.e., vi(t)≡0, i=1,…,4) when the compromised state measurement is available for feedback

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Fig. 11

System trajectories of each agent in Fig. 10

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Fig. 12

System performance for a group of agents in Example 2 with the proposed local controller given by Eq. (10) and the local corrective signal given by Eq. (32) when the compromised state measurement is available for feedback

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Fig. 13

System trajectories of each agent in Fig. 11

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Fig. 14

Time evolution of Eq. (37) in Example 2 with the proposed local controller given by Eq. (10) and the local corrective signal given by Eq. (32) when the compromised state measurement is available for feedback

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Fig. 15

Time evolution of Eq. (38) in Example 2 with the proposed local controller given by Eq. (10) and the local corrective signal given by Eq. (32) when the compromised state measurement is available for feedback

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Fig. 2

Effect of μ and γ on the ultimate bounds given by Eqs. (39) and (40) (arrow directions denote the increase of γ from 0.1 to 100)

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Fig. 3

Nominal system performance for a group of agents in Example 1 with the local controller given by Eq. (7) (i.e., vi(t)≡0, i=1,…,4) when the uncompromised state measurement is available for feedback

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Fig. 8

Nominal system performance for a group of agents in Example 2 with the local controller given by Eq. (7) (i.e., vi(t)≡0, i=1,…,4) when the uncompromised state measurement is available for feedback

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Fig. 9

System trajectories of each agent in Fig. 8

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