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Related Experiment Video

Updated: Jul 3, 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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Neural network parameter identification-based prescribed-time adaptive control for morphing glide aircraft.

Lixin Liu¹, Huijin Fan¹, Lei Liu¹

¹National Key Laboratory of Multispectral Information Intelligent Processing Technology, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China.

ISA Transactions

|July 1, 2026

Summary

This study introduces a novel neural network (NN) control method for morphing glide aircraft (MGA). The approach ensures precise attitude tracking within a set time, overcoming challenges from changing aerodynamics and uncertainties.

Keywords:

Morphing glide Aircraft Neural Network Parameter Identification Prescribed-time Sliding Mode Control

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Area of Science:

Aerospace Engineering
Control Systems Theory
Artificial Intelligence

Background:

Morphing Glide Aircraft (MGA) exhibit adaptable aerodynamic configurations for complex missions.
MGAs face challenges due to dynamic variations, aerodynamic uncertainties, and lumped disturbances.
Existing control methods struggle with singularity and unbounded control efforts in adaptive systems.

Purpose of the Study:

To develop a robust adaptive control strategy for MGAs operating under uncertain aerodynamic conditions.
To ensure finite-time convergence of control errors, independent of initial states and parameters.
To address limitations in existing stability theorems for non-smooth control systems.

Main Methods:

A novel time-scale function is introduced to prevent singularity and unbounded control growth.
A fractional-power prescribed-time Lyapunov stability theorem is established for robust analysis.
A Neural Network (NN) parameter identification method is proposed to handle aerodynamic uncertainties.
Prescribed-time adaptive sliding mode control is designed using NN identification.

Main Results:

The proposed time-scale function effectively avoids singularity and unbounded control effort.
The fractional-power stability theorem provides a foundation for advanced controller design.
The NN identification accurately estimates aerodynamic parameters under uncertainty.
The prescribed-time adaptive sliding mode control guarantees error convergence within a specified time.

Conclusions:

The developed NN-based prescribed-time adaptive control is effective for MGA attitude tracking.
The method successfully addresses dynamic variations and parameter uncertainties in MGAs.
This approach offers a robust solution for controlling adaptable aircraft in complex environments.