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Distributed data‐driven observer for linear time invariant systems
Author(s) -
Alipouri Yousef,
Zhao Shunyi,
Huang Biao
Publication year - 2020
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.3100
Subject(s) - observer (physics) , computer science , state observer , process (computing) , stability (learning theory) , lti system theory , control theory (sociology) , state (computer science) , distributed database , distributed computing , invariant (physics) , linear system , algorithm , mathematics , artificial intelligence , control (management) , machine learning , mathematical analysis , physics , quantum mechanics , nonlinear system , mathematical physics , operating system
Summary This paper is concerned with distributed data‐driven observer design problem. The existing data‐driven observers rely on a common assumption that all the information about the system, and the calculations based upon this information are centralized. Therefore the resulting algorithms cannot be applied to the distributed systems in which each local observer receives only a part of the output signal. On the other hand, traditional model‐based distributed state estimation methods generally assume that the processes are decomposed according to the known process models, while in data‐driven approaches there is no such information available. The main goal of this paper is to extend the centralized data‐driven observer design approach to the distributed framework. The stability of the proposed data‐driven distributed observer is also proved analytically. A quadruple‐tank process is simulated to demonstrate the performance of the proposed scheme.