Quantized Event-triggered Sampled-data Average Consensus with Guaranteed Rate of Convergence
Amir Amini, Amir Asif, Arash Mohammadi
Abstract
The paper proposes a novel distributed, sampled-data, event-triggered algorithm with quantized information exchange for average consensus (Q-CEASE) in multi-agent/multi-sensor networks. Q-CEASE communicates quantized information with its neighbouring nodes only if a discretized event-triggering condition is satisfied. Both design and implementation of Q-CEASE are distributed and do not require a fusion center. The design stage determines its operating region in terms of the sampling period and transmission thresholds for the constituent nodes. A minimum exponential rate for consensus convergence is guaranteed using the Lyapunov stability theorem. The performance of the Q-CEASE algorithm is quantified through Monte-Carlo simulations on randomized networks.
BibTeX
@inproceedings{icassp2019_quantizedeventtr,
title = {Quantized Event-triggered Sampled-data Average Consensus with Guaranteed Rate of Convergence},
author = {Amir Amini and Amir Asif and Arash Mohammadi},
booktitle = {ICASSP 2019},
year = {2019}
}