Resilient consensus for time-varying networks of dynamic agents

Saldaña, David; Prorok, Amanda; Sundaram, Shreyas; Campos, Mário F. M.; Kumar, Vijay

doi:10.23919/acc.2017.7962962

Cited by 99 publications

(65 citation statements)

References 13 publications

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“…In each graph G j , ∀i ∈ V each agent i sends its state information to the agents depicted. a reference altitude for unmanned aerial vehicles, a reference rendezvous time for multiple unmanned ground vehicles, and a reference radius for a circular patrolling path [4], to name only a few.…”

Section: Simulationsmentioning

confidence: 99%

Resilient Leader-Follower Consensus to Arbitrary Reference Values in Time-Varying Graphs

Usevitch

Panagou

2020

IEEE Trans. Automat. Contr.

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Several algorithms in prior literature have been proposed which guarantee consensus of normally behaving agents in a network that may contain adversarially behaving agents. These algorithms guarantee that the consensus value lies within the convex hull of initial normal agents' states, with the exact consensus value possibly being unknown. In leader-follower consensus problems however, the objective is for normally behaving agents to track a reference state that may take on values outside of this convex hull. In this paper we present methods for agents in time-varying graphs with discrete-time dynamics to resiliently track a reference state propagated by a set of leaders despite a bounded subset of the leaders and followers behaving adversarially. Our results are demonstrated through simulations.

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Section: Simulationsmentioning

confidence: 99%

Resilient Leader-Follower Consensus to Arbitrary Reference Values in Time-Varying Graphs

Usevitch

Panagou

2020

IEEE Trans. Automat. Contr.

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“…Finally, we define misbehaving agents as follows: Definition 3: An agent j ∈ V is misbehaving if both of the following conditions are satisfied: 1) There exists a time t where agent j does not update its state according to equation (4) 2) There exists a time t where agent j does not update its state according to (3) and/or sends different state values to different out-neighbors at any time t. Misbehaving agents therefore include agents that are Byzantine and malicious, as defined in [2], [22]. Briefly, the key difference between the two is that malicious agents send the same state value to all out-neighbors while Byzantine agents may send different values to different out-neighbors.…”

Section: A Problem Definitionmentioning

confidence: 99%

“…For our simulations, we consider agents in k-circulant digraphs. We assume the agents are trying to come to agreement on a state variable of interest such as altitude, the radius of a circular patrolling path ( [3]), minimum inter-agent separation distance, etc. The implementation of the W-MSR algorithm among all agents serves as a distributed consensus manager to allow agents to reach agreement on the variable of interest.…”

Section: Simulationmentioning

confidence: 99%

Resilient Leader-Follower Consensus to Arbitrary Reference Values

Usevitch

Panagou

2018

2018 Annual American Control Conference (ACC)

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The problem of consensus in the presence of misbehaving agents has increasingly attracted attention in the literature. Prior results have established algorithms and graph structures for multi-agent networks which guarantee the consensus of normally behaving agents in the presence of a bounded number of misbehaving agents. The final consensus value is guaranteed to fall within the convex hull of initial agent states. However, the problem of consensus tracking considers consensus to arbitrary reference values which may not lie within such bounds. Conditions for consensus tracking in the presence of misbehaving agents has not been fully studied. This paper presents conditions for a network of agents using the W-MSR algorithm to achieve this objective.

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“…where the second inequality follows by (26). Thus, this node i is moved out of set X 1 (k, k + 1) at time k + 1.…”

Section: Protocolmentioning

confidence: 99%

Resilient Consensus Through Event-Based Communication

Wang

Ishii

2020

IEEE Trans. Control Netw. Syst.

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We consider resilient versions of discrete-time multiagent consensus in the presence of faulty or even malicious agents in the network. In particular, we develop event-triggered update rules which can mitigate the influence of the malicious agents and at the same time reduce the communication. Each regular agent updates its state based on a given rule using its neighbors' information. Only when the triggering condition is satisfied, the regular agents send their current states to their neighbors. Otherwise, the neighbors will continue to use the state received the last time. Assuming that a bound on the number of malicious nodes is known, we propose two update rules with event-triggered communication. They follow the socalled mean subsequence reduced (MSR) type algorithms and ignore values received from potentially malicious neighbors. We characterize the necessary connectivity in the network for the algorithms to perform correctly, which are stated in terms of the notion of graph robustness. A numerical example is provided to demonstrate the effectiveness of the proposed approach.

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Resilient consensus for time-varying networks of dynamic agents

Cited by 99 publications

References 13 publications

Resilient Leader-Follower Consensus to Arbitrary Reference Values in Time-Varying Graphs

Resilient Leader-Follower Consensus to Arbitrary Reference Values in Time-Varying Graphs

Resilient Leader-Follower Consensus to Arbitrary Reference Values

Resilient Consensus Through Event-Based Communication

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