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Deep Robust Moving Horizon Estimation for Nonlinear Multi-Rate Systems.

Rusheng Wang1,2,3, Songtao Wen1,2,3, Bo Chen1,2,3

  • 1Department of Automation, Zhejiang University of Technology, Hangzhou 310023, China.

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Summary
This summary is machine-generated.

This study introduces a novel moving horizon estimation (MHE) for asynchronous multi-rate nonlinear systems. The method uses deep learning to improve state estimation accuracy in systems with time-varying parameters.

Keywords:
deep learningmoving horizon estimationnonlinear multi-rate systemrobust stability

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Area of Science:

  • Control Systems Engineering
  • Nonlinear System Analysis
  • Machine Learning Applications

Background:

  • Asynchronous multi-rate systems present challenges for traditional state estimation.
  • Accurate state estimation is crucial for control and monitoring in complex systems.

Purpose of the Study:

  • To develop a robust state estimation method for asynchronous multi-rate nonlinear systems.
  • To enhance the Moving Horizon Estimation (MHE) strategy for improved performance and adaptability.

Main Methods:

  • Pseudo-measurement synchronization modeling to convert asynchronous systems to synchronous ones.
  • A time-discounted quadratic objective Moving Horizon Estimation (MHE) strategy.
  • Deep learning framework with barrier-function regularization to learn MHE weighting parameters.

Main Results:

  • Exponential stability of the proposed MHE is established under detectability assumptions.
  • Linear Matrix Inequality (LMI) constraints for MHE feasibility are derived.
  • The deep learning approach effectively approximates and learns MHE weighting parameters, ensuring feasibility.

Conclusions:

  • The proposed MHE strategy offers improved state estimation for asynchronous multi-rate nonlinear systems.
  • The integration of deep learning and barrier functions enhances robustness against model mismatch.
  • The method is validated through a practical target tracking example.