U. A. Khan, S. Kar and J. M. F. Moura, "Distributed average consensus:
Beyond the realm of linearity," in 43rd IEEE Asilomar
Conference on Signals, Systems, and Computers, Pacific
Grove, CA, Nov. 2009, pp. 1337-1342.
In this paper, we present a distributed average-consensus algorithm with non-linear updates. In particular, we use a weighted combination of the sine of the state differences among the nodes as a consensus update instead of the conventional linear update that just includes a weighted combination of the state differences. The non-linear update comes from the theory of non-linear iterative algorithms that we present elsewhere. We show the non-linear average-consensus converges to the initial average under appropriate conditions on the weights. By simulations, we show that the convergence rate of our algorithm outperforms the conventional linear case.