I. Introduction
Consensus is a key tool for biological as well as technological distributed collaborative systems. Collective decisions and cooperative behaviors of biological networks are frequently the results of repeated local interactions, and indeed consensus decisions play a fundamental role in the lives of social animals [1]. Inspired by this, there has been an impressive amount of research on iterative algorithms, based on local data, which make the group evolution reach an agreement or consensus on a variable [2]–[4]. In many contexts, seeking consensus is an enabling condition for the prosecution of the group toward a common goal [3], [4]. In this respect, the convergence rate toward consensus is a fundamental aspect [4], and standard consensus strategies may be unsatisfactory [5]. The problem of accelerating consensus has been the subject of a good number of papers. Several methods have been proposed, ranging from the optimal design of the coupling coefficients [6] to the use of additional memory slots [7], [8]. However, most of the results deal with the simple case when agents are described by integrators and a few with higher-order integrators.