Distributed moving horizon fusion estimation for linear constrained uncertain systems

In this paper, the distributed moving horizon fusion estimation of uncertain systems with constraints of system noise and state variables is studied. Firstly, relying on the basic idea of consensus algorithm, the cost function in the performance index is reconstructed by weighted fusion of the state...

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Bibliographic Details
Published in:Asian journal of control Vol. 26; no. 6; pp. 3027 - 3039
Main Authors: Wang, Shoudong, Xue, Binqiang
Format: Journal Article
Language:English
Published: Hoboken Wiley Subscription Services, Inc 01.11.2024
Subjects:
Algorithms
consensus algorithm
Cost function
Error analysis
moving horizon estimation
Noise prediction
Observability (systems)
Optimization
Parameter uncertainty
performance index
Performance indices
Regularization
regularization method
stability analysis
State estimation
uncertain parameters
ISSN:1561-8625, 1934-6093
Online Access:Get full text
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Summary:In this paper, the distributed moving horizon fusion estimation of uncertain systems with constraints of system noise and state variables is studied. Firstly, relying on the basic idea of consensus algorithm, the cost function in the performance index is reconstructed by weighted fusion of the state prediction values. Secondly, considering the performance index with uncertain parameters, the min‐max optimization problem of the algorithm is transformed into the least squares optimization problem based on 2‐norm regularization method. Thirdly, the scalar‐weighted linear minimum variance fusion estimation strategy is used to realize the weighted fusion of local state estimation values. Then, on the premise of minimum network connectivity and collective observability, the stability of the proposed algorithm is studied, and the sufficient conditions for the expected convergence of the fused estimation error norm square are given. Finally, the effectiveness of the algorithm is verified by numerical simulation.
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ISSN:1561-8625
1934-6093
DOI:10.1002/asjc.3388