Discrete-time distributed average tracking for noisy reference signals
Changjiang Chen, Fei Chen · 2019
We consider the distributed average tracking (DAT) problem for multiple time-varying signals subject to both process and measurement noise under a directed time-varying communication network. In order to attenuate the effect of the noise, we construct a Kalman filter for each agent, based on which we design a discrete-time consensus-based DAT algorithm. It is shown that the proposed algorithm can track the average of the reference signals with small steady-state error. A numerical example is presented to validate the theoretical results.