Decentralized moving horizon estimation for large-scale networks of interconnected unconstrained linear systems

Leonardo Pedroso (Corresponding author), Pedro Batista

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)

Abstract

This paper addresses the problem of designing a decentralized state estimation solution for a large-scale network of interconnected unconstrained linear time invariant (LTI) systems. The problem is tackled in a novel moving horizon estimation (MHE) framework, while taking into account the limited communication capabilities and the restricted computational power and memory, which are distributed across the network. The proposed design is motivated by the fact that, in a decentralized setting, a Luenberguer-based framework is unable to leverage the full potential of the available local information. A method is derived to solve a relaxed version of the resulting optimization problem. It can be synthesized offline and its stability can be assessed prior to deployment. It is shown that the proposed approach allows for significant improvement on the performance of recent Luenberger-based filters. Furthermore, we show that a state-of-the-art distributed MHE solution with comparable requirements underperforms in comparison to the proposed solution.

Original languageEnglish
Article number10042003
Pages (from-to)1855-1866
Number of pages12
JournalIEEE Transactions on Control of Network Systems
Volume10
Issue number4
DOIs
Publication statusPublished - Dec 2023
Externally publishedYes

Keywords

  • Control systems
  • Couplings
  • Decision/Estimation Theory
  • Distributed Algorithms/Control
  • Estimation
  • Linear systems
  • Moving Horizon Estimation
  • Networked Control Systems
  • Networks of Autonomous Agents
  • Observers
  • Optimization
  • Sensors
  • distributed algorithms/control
  • Decision/estimation theory
  • networks of autonomous agents
  • networked control systems
  • moving horizon estimation (MHE)

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