IRI - Set-membership-based distributed moving horizon estimation of large-scale systems

Publication

Set-membership-based distributed moving horizon estimation of large-scale systems

Journal Article (2022)

Journal

ISA Transactions

Pages

402-413

Volume

128

Doc link

https://doi.org/10.1016/j.isatra.2021.10.036

File

Download the digital copy of the doc pdf document

Authors

Abstract

This work is concerned with the design of a two-step distributed state estimation scheme for large-scale systems in the presence of unknown-but-bounded disturbances and noise. The set-membership approach is employed to construct a compact set containing the states consistent with system measurements and bounded noise and disturbances. The tightened feasible region is then provided to a moving horizon estimator that determines the optimal state estimates. Partitioning of the overall problem and coordination of the resulting subproblems are achieved using decomposition of the optimality conditions and community detection. The proposed strategy is tested on a case study based on a reactor–separator system widely used in the literature. Its performance is compared to those of centralized and distributed (without set-membership) implementations, allowing to highlight its effectiveness.

Author keywords

Large-scale systems, Distributed state estimation, Moving horizon estimation, Set-membership, Optimality condition decomposition, Community detection

Scientific reference

P. Segovia, V. Puig and E. Duviella. Set-membership-based distributed moving horizon estimation of large-scale systems. ISA Transactions, 128: 402-413, 2022.