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Consensus-based distributed receding horizon estimation.

Zenghong Huang1, Weijun Lv1, Hui Chen1

  • 1Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control, School of Automation, Guangdong University of Technology, Guangzhou 510006, China.

ISA Transactions
|November 10, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a new distributed state estimation method for sensor networks using receding horizon estimation (RHE). The approach reduces computation for each node while ensuring estimation error stability.

Keywords:
ConsensusDistributed estimationReceding horizon estimationSensor networks

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Control Systems

Background:

  • Distributed state estimation is crucial for sensor networks.
  • Receding horizon estimation (RHE) offers a powerful framework for estimation problems.
  • Traditional RHE can be computationally intensive, especially in distributed systems.

Purpose of the Study:

  • To develop a computationally efficient distributed state estimation algorithm for sensor networks.
  • To leverage a novel centralized RHE scheme for distributed implementation.
  • To ensure the stability of estimation errors in the distributed RHE framework.

Main Methods:

  • A new centralized receding horizon estimation (RHE) scheme is proposed, focusing on decomposition terms.
  • A distributed estimation algorithm is derived from the centralized RHE.
  • The algorithm avoids quadratic programming (QP) by utilizing the analytical solution of centralized RHE.
  • Consensus steps are incorporated to generalize the distributed estimation for each node.

Main Results:

  • The proposed distributed algorithm significantly reduces computational load on individual sensor nodes.
  • Under collective observability, the algorithm guarantees the stability of estimation error with sufficient consensus steps.
  • Simulation results demonstrate the effectiveness of the developed distributed RHE method.

Conclusions:

  • The novel distributed state estimation algorithm based on RHE is computationally efficient.
  • The method provides stable estimation error performance in sensor networks.
  • This work offers a practical solution for distributed state estimation challenges.