Minimum-Variance Unbiased Unknown Input and State Estimation for Multi-Agent Systems by Distributed Cooperative Filters | Zendy

Changqing Liu | Zendy; Youqing Wang | Zendy; Donghua Zhou | Zendy; Xiao Shen | Zendy

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Minimum-Variance Unbiased Unknown Input and State Estimation for Multi-Agent Systems by Distributed Cooperative Filters

Author(s) -

Changqing Liu,

Youqing Wang,

Donghua Zhou,

Xiao Shen

Publication year - 2018

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2018.2815662

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

This paper addressed the problem of the simultaneous estimation of unknown inputs and states in a multi-agent system with time-invariant and time-varying topology. A group of distributed cooperative recursive filters, in the sense of minimum-variance unbiased, was developed, where the estimations of unknown input and state were combined. A necessary and sufficient existing condition is presented and proven for the proposed distributed cooperative filters. Theoretical and numerical analyses demonstrate that the existing condition of the proposed filters is significantly relaxed, in comparison to that of conventional decentralized filters.

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