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Modeling and Flight Experiments for Swarms of High Dynamic UAVs: A Stochastic Configuration Control System with

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Summary

This study introduces a novel stochastic model for Unmanned Aerial Vehicle (UAV) swarms, enhancing formation control accuracy and robustness against real-world uncertainties. The model

Keywords:
UAV swarmconfiguration controldynamic modelmultiplicative noisesstochastic robustness analysis and designstochastic system

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Area of Science:

  • Robotics and Control Systems
  • Aerospace Engineering
  • Stochastic Systems

Background:

  • Large-scale Unmanned Aerial Vehicle (UAV) swarms require precise mathematical models for optimal performance.
  • Real-world flight operations involve uncertainties from systematic measurements and environmental factors that impact system reliability.

Purpose of the Study:

  • To develop a high-precision stochastic model for UAV swarm systems with multiplicative noises.
  • To derive a configuration control model by integrating cooperative kinematic and individual fixed-wing aircraft dynamics.
  • To design an effective estimator and controller for robust formation control under uncertainty.

Main Methods:

  • Development of an Itô stochastic model incorporating multiplicative noises to represent flight uncertainties.
  • Combination of cooperative kinematic models with simplified individual fixed-wing aircraft dynamic models.
  • Design of a closed-loop system with an estimator and controller, ensuring mean-square uniform boundedness.
  • Application of the Stochastic Robustness Analysis and Design (SRAD) method for formation property optimization.

Main Results:

  • A novel Itô stochastic model for UAV swarms with multiplicative noises was derived.
  • An integrated configuration control model was established by combining kinematic and dynamic models.
  • The designed estimator and controller demonstrated effective formation control.
  • Mean-square uniform boundedness of the closed-loop stochastic system was proven.
  • Simulation and real-world flight data validated the model's effectiveness and robustness.

Conclusions:

  • The proposed stochastic model accurately represents UAV swarm dynamics under uncertainty.
  • The developed control strategy ensures robust and precise formation flight.
  • The model's efficacy is confirmed through simulations and experimental validation with real UAV data.