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This study introduces an adaptive sliding mode controller for hexacopter drones carrying payloads. The novel observer estimates payload motion, reducing oscillations and improving trajectory tracking in disturbed environments.

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Area of Science:

  • Robotics
  • Control Systems
  • Aerospace Engineering

Background:

  • Hexacopter unmanned aerial vehicles (UAVs) require precise trajectory tracking, especially when carrying payloads.
  • Payload oscillations can degrade flight performance and control accuracy.
  • Traditional controllers struggle with unmeasured payload dynamics and external disturbances.

Purpose of the Study:

  • To develop an observer-based adaptive sliding mode controller for hexacopter UAVs.
  • To estimate and compensate for payload motion and external disturbances without dedicated sensors.
  • To enhance trajectory tracking performance and reduce payload oscillations.

Main Methods:

  • Design of an extended high-gain observer for full-state and disturbance estimation.
  • Implementation of an adaptive sliding mode control strategy.
  • Compensation of estimated disturbances into the control loop.
  • Stability analysis using Lyapunov theory.

Main Results:

  • Effective estimation of payload motion and system disturbances.
  • Significant reduction in payload oscillations and improved trajectory tracking.
  • Demonstrated robustness against bounded perturbations.
  • Reduced chattering compared to conventional methods.
  • Faster payload dampening observed in simulations.

Conclusions:

  • The proposed observer-based adaptive sliding mode controller enhances hexacopter UAV performance.
  • The controller effectively manages payload dynamics and environmental perturbations.
  • This approach offers improved flight control and stability for payload-carrying UAVs.