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Related Experiment Video

Updated: Jun 16, 2026

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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Published on: October 28, 2022

Improved adaptive neural network motion control for an aero-engine hydraulic system.

Jixiang Chen¹, Xian Du², Shuanghe Yu¹

¹College of Marine Electrical Engineering, Dalian Maritime University, Dalian, 116026, China.

ISA Transactions

|June 13, 2026

Summary

This study introduces an event-triggered radial-basis-function neural network (RBF-NN) control for aero-engine hydraulic systems. It enhances control accuracy and reduces computational load, ensuring system safety and performance under disturbances.

Keywords:

Aero-engine hydraulic system Event-triggered control Motion control Uncertainty observation

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Area of Science:

Aerospace Engineering
Control Systems
Artificial Intelligence

Background:

Aero-engine hydraulic systems face uncertainties like complex aerodynamic forces and resource limitations.
These disturbances impact system safety and performance.
Existing control methods may struggle with limited bandwidth and computational power.

Purpose of the Study:

To develop a novel control scheme for aero-engine hydraulic actuators.
To address unknown disturbances and resource constraints effectively.
To improve system performance while minimizing bandwidth occupation.

Main Methods:

Utilized a radial-basis-function (RBF) neural network (NN) combined with an event-triggered technique.
Implemented an event-triggered communication protocol for NN weight updates.
Integrated a new motion control scheme with the designed RBF-NN estimator.

Main Results:

The proposed controller ensures all closed-loop signals are ultimately bounded.
Accurate trajectory tracking of the system output is achieved.
The event-triggered approach prevents Zeno behavior and optimizes resource usage.

Conclusions:

The combined RBF-NN and event-triggered control scheme is effective for aero-engine hydraulic systems.
The method enhances estimation accuracy without excessive computational cost.
Experimental validation on a Hardware-in-the-Loop platform confirms the control scheme's validity and benefits.