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Information flow-based UAV flight control adaptive approximate modeling for perturbation generalization.

Runzhu Wang1, Yi Li1, Mengfan Liu1

  • 1School of Systems Science and Engineering, Sun Yat-sen University, Guangzhou, 510275, China.

ISA Transactions
|December 31, 2025
PubMed
Summary

This study introduces an Information Flow Metamodel-Based Adaptive Approximate Modeling (IFM-AAM) framework to enhance unmanned aerial vehicle (UAV) flight control systems against diverse threats. The novel approach improves control performance and real-time adaptability under various perturbations.

Keywords:
Adaptive mechanismApproximate modelingInformation flowPerturbation generalizationUAV flight control system

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Area of Science:

  • Aerospace Engineering
  • Control Systems Engineering
  • Cybersecurity

Background:

  • Unmanned aerial vehicle (UAV) flight control systems face security threats like environmental disturbances, component faults, and cyber-attacks.
  • Current fault-tolerant control methods struggle with generalizability for heterogeneous perturbations, especially adversarial cyber-attacks, due to specific modeling assumptions.

Purpose of the Study:

  • To propose a novel Information Flow Metamodel-Based Adaptive Approximate Modeling (IFM-AAM) framework.
  • To address the limitations of existing methods in representing and mitigating diverse security threats in UAV flight control.
  • To enhance the generalizability and adaptability of control systems against heterogeneous perturbations.

Main Methods:

  • Developed an Information Flow Metamodel (IFM) for high-level structural abstraction and approximate modeling.
  • Represented heterogeneous perturbations as symbolic anomalies within a unified information flow structure for consistent analysis.
  • Integrated a lightweight Actor-Critic reinforcement learning (RL) mechanism for real-time adaptive tuning of control parameters within domain models (DMs).

Main Results:

  • IFM-AAM framework demonstrated effective approximation of control performance in nominal and diverse perturbation scenarios through comparative simulations.
  • Validated the unified representation and propagation analysis for heterogeneous perturbations.
  • Onboard computer validations confirmed the real-time performance and low computational overhead of the developed domain models.

Conclusions:

  • The proposed IFM-AAM framework provides an effective unified approach for representing and analyzing heterogeneous perturbations in UAV flight control.
  • The framework enhances control system adaptability and robustness against a wide range of threats.
  • The method offers a computationally efficient solution for real-time applications in UAVs.