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Genetic Optimization-Based Consensus Control of Multi-Agent 6-DoF UAV System.

Aws Abdulsalam Najm1, Ibraheem Kasim Ibraheem1, Ahmad Taher Azar2,3

  • 1Department of electrical engineering, College of engineering, University of Baghdad, Baghdad 10001, Iraq.

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|July 1, 2020
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Summary

This study introduces a consensus control law for multi-agent quadrotor systems using a genetic algorithm (GA) for parameter tuning. The proposed method effectively achieves desired formations and trajectory tracking in simulations.

Keywords:
active disturbance rejection controlhybrid control systemnonlinear PIDquadrotor systemunmanned aerial vehicle

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Area of Science:

  • Robotics
  • Control Systems
  • Artificial Intelligence

Background:

  • Coordinated control of multi-agent systems, particularly quadrotors, is crucial for complex tasks.
  • Leader-follower topologies present unique challenges in achieving consensus and formation control.
  • Optimization techniques are needed to tune complex control parameters effectively.

Purpose of the Study:

  • To propose a novel consensus control law for a three-agent quadrotor system with a leader-follower topology.
  • To utilize the genetic algorithm (GA) for optimizing consensus control parameters.
  • To validate the controller's effectiveness through simulations using nonlinear models and various test cases.

Main Methods:

  • Development of a consensus control law for a three-quadrotor system.
  • Application of the genetic algorithm (GA) to tune control parameters.
  • Simulation studies employing the complete nonlinear quadrotor model and simplified models for controller design.
  • Testing under diverse scenarios including trajectory tracking, formation control, and dynamic topology switching.

Main Results:

  • The proposed consensus control law demonstrated effective performance across all simulated scenarios.
  • The genetic algorithm successfully tuned the control parameters for optimal system behavior.
  • The multi-agent system achieved consensus and maintained desired formations reliably.
  • Robustness was observed during trajectory tracking and topology switching maneuvers.

Conclusions:

  • The developed consensus control law, optimized via GA, is effective for multi-agent quadrotor systems.
  • The leader-follower topology with the proposed control strategy enables stable formation control and trajectory tracking.
  • The study validates the practical applicability of the approach in complex aerial robotic systems.