Resilience against Misbehaving Nodes in Self-triggered Coordination Networks

Senejohnny, Danial; Sundaram, Shreyas; Persis, Claudio De; Tesi, Pietro

doi:10.1109/cdc.2018.8619599

Cited by 8 publications

(10 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, from (12a) and (24), we can say that E{s i (t + 1)|F t } is a convex combination of s i (t) and s j (t), j ∈ N i (p, t), and thus based on similar arguments in the proof of Theorem 1 for (15) and (16),…”

Section: Appendixmentioning

confidence: 95%

“…In this condition, considering (37) and (38) in the protocol (24), there exists a nonzero probability such that s i (t) leaves a neighborhood determined in (33) which has a contradiction with almost sure convergence of s i (t) described in (32). Therefore, we should have s ai = s aj , i, j ∈ V h , and therefore from (32), we have…”

Section: Appendixmentioning

confidence: 99%

“…In [22], that idea is employed for resilient synchronization of highorder MASs. In [23] and [24], resilient consensus control of MASs with asynchronous update time is investigated. In [25], the resilient max consensus problem is studied.…”

Section: A State Of the Art And Existing Problemsmentioning

confidence: 99%

“…Based on (24) and according to (12a), the healthy agents safety variables will be updated such that s i (t + 1) will be a convex combination of s i (t) ands ij (t), j ∈ N i (p, t, ω), while the set N i (p, t, ω) is determined with stochastic errors due to noises. Indeed, in stochastic analysis of the evolution of s i (t), i ∈ V h , we should note that some a ij (p, t, ω), j ∈ N i (p), may not be the same as them when there are no noises in the communication channels, i.e., for some j ∈…”

Section: Appendixmentioning

confidence: 99%

See 3 more Smart Citations

Almost Sure Resilient Consensus Under Stochastic Interaction:Links Failure and Noisy Channels

Rezaee

Parisini

Polycarpou

2021

IEEE Trans. Automat. Contr.

View full text Add to dashboard Cite

The resilient consensus problem over a class of discrete-time linear multiagent systems is addressed. Because of external cyber-attacks, some agents are assumed to be malicious and not following a desired cooperative behavior. Thus, the objective consists in designing a control strategy for the healthy agents to reach consensus upon their state vectors, while due to interaction among the agents, the malicious agents try to prevent them to achieve consensus. Although this problem has been investigated by some researchers, under the existing approaches in the literature, achieving consensus is only guaranteed when the information exchange among the agents is deterministic. Based on this motivation, the main contribution of the paper is on almost sure resilient consensus control of a network of healthy agents in the presence of stochastic links failure and communication noises. We design a discrete-time protocol for the set of the healthy agents, and we show that under some probabilistic conditions on interaction among the agents, achieving almost sure consensus among the healthy agents can be guaranteed. The results also are verified by numerical examples.

show abstract

Section: Appendixmentioning

confidence: 95%

Section: Appendixmentioning

confidence: 99%

Section: A State Of the Art And Existing Problemsmentioning

confidence: 99%

Section: Appendixmentioning

confidence: 99%

See 2 more Smart Citations

Almost Sure Resilient Consensus Under Stochastic Interaction:Links Failure and Noisy Channels

Rezaee

Parisini

Polycarpou

2021

IEEE Trans. Automat. Contr.

View full text Add to dashboard Cite

show abstract

“…results that draw upon these papers include state estimation [18][19][20][21], rendezvous of mobile agents [22,23], output synchronization [11], simultaneous arrival of interceptors [17], distributed optimization [29,31], reliable broadcast [33,43], clock synchronization [8], randomized quantized consensus [5], self-triggered coordination [26], and multi-hop communication [30].…”

Section: Introductionmentioning

confidence: 99%

Determining r- and (r,s)-robustness of digraphs using mixed integer linear programming

Usevitch

Panagou

2020

Automatica

View full text Add to dashboard Cite

There has been an increase in the use of resilient control algorithms based on the graph theoretic properties of r-and (r, s)robustness. These algorithms guarantee consensus of normally behaving agents in the presence of a bounded number of arbitrarily misbehaving agents if the values of the integers r and s are sufficiently large. However, determining an arbitrary graph's robustness is a highly nontrivial problem. This paper introduces a novel method for determining the r-and (r, s)robustness of digraphs using mixed integer linear programming; to the best of the authors' knowledge it is the first time that mixed integer programming methods have been applied to the robustness determination problem. The approach only requires knowledge of the graph Laplacian matrix, and can be formulated with binary integer variables. Mixed integer programming algorithms such as branch-and-bound are used to iteratively tighten the lower and upper bounds on r and s. Simulations are presented which compare the performance of this approach to prior robustness determination algorithms.

show abstract

Multi-robot Coordination and Planning in Uncertain and Adversarial Environments

Zhou

Tokekar

2021

Curr Robot Rep

View full text Add to dashboard Cite

Purpose of Review Deploying a team of robots that can carefully coordinate their actions can make the entire system robust to individual failures. In this report, we review recent algorithmic development in making multi-robot systems robust to environmental uncertainties, failures, and adversarial attacks.Recent Findings We find the following three trends in the recent research in the area of multi-robot coordination: (1) resilient coordination to either withstand failures and/or attack or recover from failures/attacks; (2) risk-aware coordination to manage the trade-off risk and reward, where the risk stems due to environmental uncertainty; (3) Graph neural networks based coordination to learn decentralized multi-robot coordination policies. These algorithms have been applied to tasks such as formation control, task assignment and scheduling, search and planning, and informative data collection. SummaryIn order for multi-robot systems to become practical, we need coordination algorithms that can scale to large teams of robots dealing with dynamically changing, failure-prone, contested, and uncertain environments. There has been significant recent research on multi-robot coordination that has contributed resilient and risk-aware algorithms to deal with these issues and reduce the gap between theory and practice. Learning-based approaches have been seen to be promising, especially since they can learn who, when, and how to communicate for effective coordination. However, these algorithms have also been shown to be vulnerable to adversarial attacks, and as such developing learning-based coordination strategies that are resilient to such attacks and robust to uncertainties is an important open area of research.

show abstract

Resilience against Misbehaving Nodes in Self-triggered Coordination Networks

Cited by 8 publications

References 22 publications

Almost Sure Resilient Consensus Under Stochastic Interaction:Links Failure and Noisy Channels

Almost Sure Resilient Consensus Under Stochastic Interaction:Links Failure and Noisy Channels

Determining r- and (r,s)-robustness of digraphs using mixed integer linear programming

Multi-robot Coordination and Planning in Uncertain and Adversarial Environments

Contact Info

Product

Resources

About