Distributed dynamic state estimation with parameter identification for large-scale systems |
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Authors: | Yibing Sun Minyue Fu Bingchang Wang Huanshui Zhang |
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Institution: | 1. School of Control Science and Engineering, Shandong University, Jinan 250061, China;2. School of Mathematical Sciences, University of Jinan, Jinan 250022, China;3. School of Electrical Engineering and Computer Science, University of Newcastle, NSW 2308, Australia;4. Guangdong Key Laboratory of IoT Information Processing, School of Automation, Guangdong University of Technology, Guangzhou 510006, China |
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Abstract: | In this paper, we consider a distributed dynamic state estimation problem for time-varying systems. Based on the distributed maximum a posteriori (MAP) estimation algorithm proposed in our previous study, which studies the linear measurement models of each subsystem, and by weakening the constraint condition as that each time-varying subsystem is observable, this paper proves that the error covariances of state estimation and prediction obtained from the improved algorithm are respectively positive definite and have upper bounds, which verifies the feasibility of this algorithm. We also use new weighting functions and time-varying exponential smoothing method to ensure the robustness and improve the forecast accuracy of the distributed state estimation method. At last, an example is used to demonstrate the effectiveness of the proposed algorithm together with the parameter identification. |
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Keywords: | Corresponding author at: School of Electrical Engineering and Computer Science University of Newcastle NSW 2308 Australia |
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