Dynamic average consensus on synchronous communication networks

We propose a class of dynamic average consensus algorithms that allow a group of agents to track the average of their measured signals. The algorithms are implemented in discrete time and require a synchronous communication schedule. The convergence results rely on the input-to-output stability properties of consensus algorithms and require that the union of communication graphs over a bounded period of time be strongly connected. The only requirement on the set of signals is that the difference of the n(th)-order derivatives of any two signals be bounded for some n >= 0.