K-filter-based distributed adaptive output cluster consensus under cooperative-competitive networks

Abstract

This paper investigates the K-filter-based output cluster consensus problem under cooperative-competitive networks. The agents in the network are divided into several clusters where the relationships among agents belonging to the same cluster are only cooperative while agents in different clusters have both cooperative and competitive interactions with each other. Firstly, K-filters are introduced to estimate the state variables of the system with unknown control coefficients. Then new neighborhood errors are constructed to make the couplings among agents belonging to the same cluster strong enough. Based on the K-filters and the new neighborhood errors, a distributed output-feedback control protocol is designed to achieve output cluster consensus. It is proved that all signals in the closed-loop systems are bounded and the output of each agent can track the reference signal asymptotically if the network is in-degree balanced and each cluster contains a directed spanning tree. Finally, a numerical example is provided to verify the effectiveness of the proposed control protocol.