A Fast Robust Nonlinear Dynamic Average Consensus Estimator in Discrete Time∗

We present a new discrete-time dynamic average consensus estimator which has significant performance advantages over existing designs. It uses nonlinear oscillators to achieve both initialization robustness and internal boundedness, and its convergence rates are comparable to those of fast static consensus methods. Its primary disadvantage is that it cannot generally track averages of unbounded inputs with bounded error.