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Related Experiment Video

Updated: Jul 8, 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

Adaptive RBF network for parameter estimation and stable air-fuel ratio control.

Shiwei Wang¹, D L Yu

¹Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK.

Neural Networks : the Official Journal of the International Neural Network Society

|January 2, 2008

Summary

This study introduces an adaptive neural network to reduce engine air-fuel ratio chattering caused by system uncertainties. The method ensures stable control and precise regulation near the stoichiometric value.

Related Experiment Videos

Last Updated: Jul 8, 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:

Automotive Engineering
Control Systems
Artificial Intelligence

Background:

Variable structure control (VSC) for engine air-fuel ratio is prone to chattering.
System uncertainties, including unknown parameters and time-varying dynamics, cause this instability.

Purpose of the Study:

To propose an adaptive neural network (ANN) method to mitigate chattering in VSC for engine air-fuel ratio.
To estimate immeasurable physical parameters online and compensate for model uncertainties and dynamic variations.

Main Methods:

An adaptive neural network is employed for online parameter estimation and uncertainty compensation.
The neural network's adaptive law is derived using Lyapunov stability theory.
Simulations are performed using a mean value engine model.

Main Results:

The proposed ANN method significantly reduces chattering in the engine air-fuel ratio.
The air-fuel ratio is effectively regulated within the desired stoichiometric range.
The Lyapunov method guarantees system stability and neural network convergence.

Conclusions:

The adaptive neural network approach provides a robust solution for controlling engine air-fuel ratio.
This technique enhances control accuracy and stability in the presence of system uncertainties.
The method is validated through computer simulations, demonstrating its practical applicability.