Distributed Moving Horizon Fusion Estimation for Nonlinear Constrained Uncertain Systems

Shoudong Wang and Binqiang Xue ()
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Shoudong Wang: School of Automation, Qingdao University, Qingdao 266100, China
Binqiang Xue: School of Automation, Qingdao University, Qingdao 266100, China

Mathematics, 2023, vol. 11, issue 6, 1-22

Abstract: This paper studies the state estimation of a class of distributed nonlinear systems. A new robust distributed moving horizon fusion estimation (DMHFE) method is proposed to deal with the norm-bounded uncertainties and guarantee the estimation performance. Based on the given relationship between a state covariance matrix and an error covariance matrix, estimated values of the unknown parameters in the system model can be obtained. Then, a local moving horizon estimation optimization algorithm is constructed by using the measured values of sensor nodes themselves, the measured information of adjacent nodes and the prior state estimates. By solving the above nonlinear optimization problem, a local optimal state estimation is obtained. Next, based on covariance intersection (CI) fusion strategy, the local optimal state estimates sent to the fusion center are fused to derive optimal state estimates. Furthermore, the sufficient conditions for the square convergence of the fusion estimation error norm are given. Finally, a simulation example is employed to demonstrate the effectiveness of the proposed algorithm.

Keywords: distributed moving horizon fusion estimation (DMHFE); nonlinear uncertain systems; covariance intersection (CI) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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