Consensus of second-order multi-agent systems in the presence of locally bounded faults

Dibaji, Seyed Mehran; Ishii, Hideaki

doi:10.1016/j.sysconle.2015.02.005

Cited by 109 publications

(86 citation statements)

References 19 publications

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“…It was first introduced by [24] for analysis of resilient consensus of real-valued first-order multi-agent systems. Related works include [9] which studied the case with delays in communication and [10], [13] for the case of agents with real-valued second-order dynamics. We use the more general notion of (r, s)-robust graphs, which plays an important role to obtain a tight necessary and sufficient condition.…”

Section: A Graph Theory Notionsmentioning

confidence: 99%

“…We introduce notions related to malicious agents and consensus in the presence of such agents [9], [10], [24]. Definition 2.3: Agent i is called normal if it updates its state based on the predefined control (4).…”

Section: Resiliency Notions and Algorithmmentioning

confidence: 99%

“…The algorithm is the quantized version of the weighted mean subsequence reduced (W-MSR) algorithm studied in [9], [24] and thus will be referred to as the QW-MSR algorithm. Using similar ideas, resilient consensus of second-order agent networks has been studied as well [10], [13]. Each normal node ignores the information received from its neighbors which may be misleading.…”

Section: Definition 24mentioning

confidence: 99%

“…The objective of the agent network is to reach resilient consensus among all agents by applying the local control rule (12) with the probabilistic quantizers under the deterministic update scheme (10). To this end, we again employ the QW-MSR algorithm in Algorithm 2.6, but this time, we interpret the neighbors' states to be the delayed information available at each update time k.…”

Section: A Problem Formulation and Algorithmmentioning

confidence: 99%

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Resilient randomized quantized consensus

Dibaji

Ishii

Tempo

2016

2016 American Control Conference (ACC)

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Abstract-We consider the problem of multi-agent consensus where some agents are subject to faults/attacks and might make updates arbitrarily. The network consists of agents taking integervalued (i.e., quantized) states under directed communication links. The goal of the healthy normal agents is to form consensus in their state values, which may be disturbed by the non-normal, malicious agents. We develop update schemes to be equipped by the normal agents whose interactions are asynchronous and subject to non-uniform and time-varying time delays. In particular, we employ a variant of the so-called mean subsequence reduced (MSR) algorithms, which have been long studied in computer science, where each normal agent ignores extreme values from its neighbors. We solve the resilient quantized consensus problems in the presence of totally/locally bounded adversarial agents and provide necessary and sufficient conditions in terms of the connectivity notion of graph robustness. Furthermore, it will be shown that randomization is essential both in quantization and in the updating times when normal agents interact in an asynchronous manner. The results are examined through a numerical example.

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Section: A Graph Theory Notionsmentioning

confidence: 99%

Section: Resiliency Notions and Algorithmmentioning

confidence: 99%

Section: Definition 24mentioning

confidence: 99%

Section: A Problem Formulation and Algorithmmentioning

confidence: 99%

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Resilient randomized quantized consensus

Dibaji

Ishii

Tempo

2016

2016 American Control Conference (ACC)

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“…This problem is related with consensus problems where malicious agents update their states arbitrarily, and a control must be designed for the honest agents that guarantees consensus; see [5] for first-order and [6] for second-order multiagent systems. The control for each agent proposed in [5], [6] discards information from the neighbors which sensibly deviates from the current state of the agent.…”

Section: Introductionmentioning

confidence: 99%

The cost of dishonesty on optimal distributed frequency control of power networks

Monshizadeh

Persis

Simpson-Porco

2016

2016 IEEE 55th Conference on Decision and Control (CDC)

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Abstract-Optimal frequency controllers for power networks based on distributed averaging have previously been shown to be an effective means of distributing control authority among agents while maintaining a globally optimal operating point. Distributed control architectures however require an implicit trust between participating agents, in that each must faithfully communicate the appropriate control variables to neighboring agents. Here we study the case where some agents attempt to "cheat the system" by adding a bias to the averaging controller in order to lower their generation cost. We quantify the effect of this dishonesty on the resource allocation problem and introduce a "cost graph" whose weights measure the effect of the bias on the optimal equilibrium. Moreover, we propose an "honesty-enforcing" controller which counteracts the dishonest agents, and restores the optimal setpoint of the network.

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An Overview of Cooperative and Consensus Control of Multiagent Systems

Davoodi

Gallehdari

Saboori

et al. 2016

Wiley Encyclopedia of Electrical and Electronics Engineering

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There has been a growing interest toward the development of networked unmanned autonomous systems that can operate without an extensive involvement of humans. The motivation for this focus can be traced to the emergence of applications where direct human intervention is not possible due to the environmental hazards, complexity of the tasks, or other restrictions. These networks can consist of a large number of dynamical systems (agents), such as unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), and unmanned underwater vehicles (UUVs). These systems commonly include a number of sensors, actuators, and decisionmakers. Therefore, the network of these systems is a network of a large number of sensors and actuators or, as is known in the literature, a system of systems (SoS). In this work, we provide a brief overview of controlling these networks, their applications, and the solutions that are proposed for these problems in the literature. Specifically, this article overviews recent results and progress made on multiagent consensus by focusing on cooperative control and consensus (formation) control in the presence of communication, control implementation, and saturation constraints. In addition, we review the active areas of event‐triggered consensus control, network security and attacks on the agents, and networks and resilient consensus control strategies that are proposed to handle these challenges.

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Consensus of second-order multi-agent systems in the presence of locally bounded faults

Cited by 109 publications

References 19 publications

Resilient randomized quantized consensus

Resilient randomized quantized consensus

The cost of dishonesty on optimal distributed frequency control of power networks

An Overview of Cooperative and Consensus Control of Multiagent Systems

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