Consensus acceleration of multi-agent systems via model prediction

A fastest consensus problem of topology fixed networks has been formulated as an optimal linear iteration problem and efficiently solved by Xiao and Boyd [1]. Considering a kind of predictive mechanism, we show that the consensus evolution can be further accelerated while physically maintaining the network topology. The underlaying mechanism is that an effective prediction is able to convert the network status along temporal dimension to that in spatial dimension and hence induce a network with a virtually denser topology. With this topology, an even faster consensus is expected to occur. The result is motivated by the predictive mechanism widely existing in biological swarms, flocks, and synchronization networks.