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Related Experiment Video

Updated: Jul 7, 2026

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

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

Published on: October 28, 2022

A mixture-of-experts framework for adaptive Kalman filtering.

W S Chaer¹, R H Bishop, J Ghosh

¹Dept. of Aerosp. Eng. & Eng. Mech., Texas Univ., Austin, TX.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|January 1, 1997

Summary

This study introduces an adaptive Kalman filtering method using a mixture-of-experts regulated by a gating network. This approach enhances estimation accuracy and adaptability in dynamic environments.

Related Experiment Videos

Last Updated: Jul 7, 2026

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

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

Published on: October 28, 2022

Area of Science:

Engineering
Computer Science

Background:

Kalman filtering is a widely used technique for state estimation.
Traditional Kalman filters assume known system parameters, limiting their performance in uncertain environments.
Adaptive filtering techniques are needed to handle time-varying or unknown system dynamics.

Purpose of the Study:

To propose a novel modular and flexible adaptive Kalman filtering approach.
To enhance estimation accuracy, responsiveness, and computational efficiency compared to existing methods.
To introduce on-line adaptation mechanisms for filter parameters.

Main Methods:

A mixture-of-experts framework regulated by a gating network.
Each expert is a Kalman filter with different parameter realizations.
Gating network adapts expert weights based on performance.
Periodic enhancement using recursive quadratic programming or genetic algorithms for parameter adaptation.

Main Results:

The proposed filter bank demonstrates superior estimation accuracy.
It exhibits a quicker response to changing environments.
It offers improved numerical stability and computational efficiency over classical methods.
Real-time implementation is feasible with the proposed parameter adaptation schemes.

Conclusions:

The proposed adaptive Kalman filtering approach offers significant advantages.
It provides a robust and efficient solution for state estimation in uncertain and dynamic systems.
The modular design and adaptive mechanisms enhance its applicability across various domains.