Search Results for author: Rachel Kalpana Kalaimani

Found 6 papers, 0 papers with code

Optimal Pinning Control for Synchronization over Temporal Networks

no code implementations14 Mar 2024 Aandrew Baggio S, Rachel Kalpana Kalaimani

Finally, we address the problem of maximizing the number of synchronized nodes when there are constraints on the number of nodes that could be pinned.

Resilient Dynamic Average Consensus based on Trusted agents

no code implementations14 Mar 2023 Shamik Bhattacharyya, Rachel Kalpana Kalaimani

Then we propose our novel Resilient Dynamic Average Consensus (ResDAC) algorithm that ensures the trusted and ordinary agents achieve resilient DAC in the presence of adversarial agents.

Adversarial Attack

ADMM based Distributed State Observer Design under Sparse Sensor Attacks

no code implementations13 Sep 2022 Vinaya Mary Prinse, Rachel Kalpana Kalaimani

This paper considers the design of a distributed state-observer for discrete-time Linear Time-Invariant (LTI) systems in the presence of sensor attacks.

Adversarial Attack

Distributed Online Optimization with Byzantine Adversarial Agents

no code implementations25 Sep 2021 Sourav Sahoo, Anand Gokhale, Rachel Kalpana Kalaimani

We study the problem of non-constrained, discrete-time, online distributed optimization in a multi-agent system where some of the agents do not follow the prescribed update rule either due to failures or malicious intentions.

Distributed Optimization

Distributed Estimation over Directed Graphs Resilient to Sensor Spoofing

no code implementations10 Apr 2021 Shamik Bhattacharyya, Kiran Rokade, Rachel Kalpana Kalaimani

This paper addresses the problem of distributed estimation of an unknown dynamic parameter by a multi-agent system over a directed communication network in the presence of an adversarial attack on the agents' sensors.

Adversarial Attack

Distributed computation of fast consensus weights using ADMM

no code implementations19 Feb 2020 Kiran Rokade, Rachel Kalpana Kalaimani

We consider the problem of achieving average consensus among multiple agents, where the inter-agent communication network is depicted by a graph.

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