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Model-free distributed state estimation with local measurements.

Kepan Gao1, Chenyu Ran1, Xiaoling Wang2,3

  • 1College of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.

Chaos (Woodbury, N.Y.)
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Summary
This summary is machine-generated.

A new model-free state estimation method uses distributed stochastic variational inference and nearest-neighbor rules to improve accuracy and speed for systems with limited, scattered sensor data.

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Area of Science:

  • Control Systems Engineering
  • Information Theory
  • Machine Learning

Background:

  • State estimation is crucial for physical plants, especially those with high-dimensional, wide-area, and scattered characteristics.
  • Limited output measurements from sensor networks pose significant challenges for traditional state estimation techniques.

Purpose of the Study:

  • To propose a novel model-free state estimation approach for systems with partial and limited output information.
  • To enhance the accuracy and speed of state estimation in complex, distributed systems.

Main Methods:

  • A distributed stochastic variational inference state estimation (DSVIE) approach is introduced.
  • Nearest-neighbor rule-based information interaction among local estimators is employed to compensate for partial measurements.
  • A model-free strategy is utilized to handle unknown system dynamics.

Main Results:

  • Numerical experiments demonstrate clear advantages of the proposed DSVIE method in estimation accuracy.
  • The method shows significant improvements in estimation speed compared to existing approaches.
  • The study validates the effectiveness of nearest-neighbor interactions for handling local output limitations.

Conclusions:

  • The proposed distributed stochastic variational inference state estimation offers a robust solution for complex systems with limited sensor data.
  • This approach provides valuable insights for improving state estimation efficiency under challenging measurement conditions.
  • The model-free nature and distributed architecture make it adaptable to various real-world applications.