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Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

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A Recursive Non-Uniform Sampling Estimator for Asynchronous Nonlinear Systems.

Yu-Hang Yang¹, Jin-Gang Liu¹, Shen-Min Song¹

¹Center for Control Theory and Guidance Technology, Harbin Institute of Technology, Harbin 150001, China.

Sensors (Basel, Switzerland)

|May 11, 2024

Summary

This summary is machine-generated.

This study addresses asynchronous estimation challenges in nonlinear systems with packet losses. It introduces a novel observation inference method for accurate state estimation, even with imperfect system models.

Keywords:

covariance intersection fusion interpolation modelling non-uniform sampling state estimation

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Area of Science:

Control Systems Engineering
Nonlinear System Analysis
Signal Processing

Background:

Asynchronous sampling and packet losses complicate state estimation in nonlinear systems.
Accurate dynamic models are often difficult to obtain due to inherent uncertainties and unmodeled dynamics.
Existing estimation methods may struggle with the combined challenges of asynchronous data and data loss.

Purpose of the Study:

To develop a robust state estimation method for randomly sampling nonlinear systems experiencing packet losses.
To address the synchronization issue in asynchronous systems by weighting state updates.
To propose an observation inference technique for improved estimation accuracy despite modeling errors.

Main Methods:

Synchronization of asynchronous sampling via state weighting of adjacent update points.
Application of the projection theorem for state estimation at sampling instances.
Development of observation inference using interpolation techniques to handle modeling uncertainties.
Extension of the algorithm for distributed fusion estimation in multi-sensor systems.

Main Results:

Successful synchronization of asynchronous sampling systems.
Effective state estimation even with packet losses at control and measurement points.
Improved estimation accuracy through observation inference, mitigating challenges from modeling errors.
Validation of a distributed fusion estimator for multi-sensor applications.

Conclusions:

The proposed observation inference method effectively handles asynchronous estimation in nonlinear systems with packet losses.
The developed algorithm provides a robust solution for state estimation in challenging system dynamics.
The extension to multi-sensor systems demonstrates the algorithm's versatility and potential for practical applications.