Asymptotic consensus of multi-agent systems under binary-valued observations and observation uncertainty
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Publication:6131452
DOI10.1016/j.sysconle.2023.105656OpenAlexW4388177164MaRDI QIDQ6131452
Chuiliu Kong, Ying Wang, Yanlong Zhao
Publication date: 5 April 2024
Published in: Systems \& Control Letters (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.sysconle.2023.105656
multi-agent systemsconsensus controlbinary-valued observationrecursive projection algorithmobservation uncertaintypolynomial-type variance
Cites Work
- Recursive identification of FIR systems with binary-valued outputs and communication channels
- System identification with quantized observations
- Identification of ARMA models using intermittent and quantized output observations
- Distributed recursive projection identification with binary-valued observations
- Recursive projection algorithm on FIR system identification with binary-valued observations
- Asymptotically efficient parameter estimation using quantized output observations
- Quantized consensus
- Projected gradient methods for linearly constrained problems
- Consensus Protocol for Multiagent Systems With Undirected Topologies and Binary-Valued Communications
- Quantized Identification With Dependent Noise and Fisher Information Ratio of Communication Channels
- Consensus of Multi-Agent Systems Under Binary-Valued Measurements and Recursive Projection Algorithm
- Coordination and Consensus of Networked Agents with Noisy Measurements: Stochastic Algorithms and Asymptotic Behavior
- System identification using binary sensors
- Coordination of groups of mobile autonomous agents using nearest neighbor rules
- Distributed Consensus With Limited Communication Data Rate
- Distributed Coordination of Multi-Agent Systems With Quantized-Observer Based Encoding-Decoding
- Identification of FIR systems with binary-valued observations against denial-of-service attacks
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