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

Pablo Segovia*, Vicenç Puig, Eric Duviella

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
8 Downloads (Pure)


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.

Original languageEnglish
Number of pages12
JournalISA Transactions
Publication statusAccepted/In press - 2022


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


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