Decentralized adaptive filtering for multi-agent systems with uncertain couplings

In this paper, the problem of decentralized adaptive filtering for multi-agent systems with uncertain couplings is formulated and investigated. This problem is challenging due to the mutual dependency of state estimation and coupling estimation. First, the problem is divided into four typical types based on the origin of coupling relations and linearity of the agent dynamics. Then models of the four types are given and the corresponding decentralized adaptive filtering algorithms are designed for the purpose of estimation of the unknown states and couplings which denotes the relations between agents and their neighbor agents in terms of states or outputs simultaneously, with preliminary stability analysis and discussions. For testing the effects of algorithm, with the so-called certainty-equivalence principle, control signals are designed based on the results of state estimation and coupling estimation got by the proposed decentralized adaptive filtering algorithms. Extensive simulations are conducted to verify the effectiveness of considered algorithms.