QUANTIZED EVENT-TRIGGERED SAMPLED-DATA AVERAGE CONSENSUS WITH GUARANTEED RATE OF CONVERGENCE

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.