A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 1 Issue 1
Jan.  2014

IEEE/CAA Journal of Automatica Sinica

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Article Contents
Hongbin Ma, Yini Lv, Chenguang Yang and Mengyin Fu, "Decentralized Adaptive Filtering for Multi-agent Systems with Uncertain Couplings," IEEE/CAA J. of Autom. Sinica, vol. 1, no. 1, pp. 101-112, 2014.
Citation: Hongbin Ma, Yini Lv, Chenguang Yang and Mengyin Fu, "Decentralized Adaptive Filtering for Multi-agent Systems with Uncertain Couplings," IEEE/CAA J. of Autom. Sinica, vol. 1, no. 1, pp. 101-112, 2014.

Decentralized Adaptive Filtering for Multi-agent Systems with Uncertain Couplings

Funds:

This work was supported by National Natural Science Foundation of China (NSFC) (61004059, 61004139, 61031001), China-UK NSFC-RS Joint Project (61211130359), the EU Marie Curie Project (PIIFR-GA-20 10-910078- H2R), Beijing Outstanding Talents Programme (2012D00 901 1000003), and Graduate Teaching/Innovation Funding of Beijing Institute of Technology.

  • 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.

     

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