Nonlinear average consensus based on weight morphing.

We present a novel weight design for average consensus that improves its transient and stead-state performance. The idea is to blend Metropolis-Hastings weights and convex-optimization weights via signal-adaptive morphing coefficients. The resulting scheme is shown to be particularly useful in dynamic scenarios where the measurements feature abrupt changes or unknown noise levels. [ABSTRACT FROM PUBLISHER]