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Wind Preview-Based Model Predictive Control of Multi-Rotor UAVs Using LiDAR.

Arthur P Mendez1, James F Whidborne1, Lejun Chen2

  • 1Dynamics Simulation and Control Group, Cranfield University, Cranfield MK43 0AL, UK.

Sensors (Basel, Switzerland)
|April 13, 2023
PubMed
Summary
This summary is machine-generated.

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This study enhances autonomous drone docking by using LiDAR wind preview with Model Predictive Control (MPC). This improves position-holding performance by 80% and enables precision landings in windy conditions.

Area of Science:

  • Robotics and Control Systems
  • Aerospace Engineering
  • Sensor Technology

Background:

  • Autonomous Unmanned Aerial Vehicles (UAVs) face challenges in outdoor operations due to wind gusts, impacting position-holding and automated docking.
  • Advancements in remote sensing, like LiDAR, enable real-time wind preview, offering potential for improved UAV control.

Purpose of the Study:

  • To propose and evaluate a wind-preview-based Model Predictive Controller (MPC) for UAV gust rejection using LiDAR wind measurements.
  • To enhance the precision landing capabilities of autonomous quadrotors in the presence of wind disturbances.

Main Methods:

  • A ground-based LiDAR system was employed to provide real-time wind disturbance predictions.
  • A Model Predictive Controller (MPC) was designed to utilize this wind preview information for optimal disturbance compensation.
Keywords:
MPCVTOL aircraftgust rejectionoptimal controlpreview controlquadrotorremote wind sensing

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  • Simulations were performed using field-gathered LiDAR data to validate the controller's performance and robustness.

    • Main Results:

    • The wind-preview-based MPC demonstrated an 80% improvement in position-holding performance compared to feedback-only MPC.
    • Reduced rotational rates and peak angles were observed, indicating a more stable and less aggressive response to wind gusts.
    • Robustness tests showed a significant margin (1.75s timing, 120% strength) before performance degradation.

    Conclusions:

    • Integrating LiDAR-based wind preview into MPC significantly enhances UAV gust rejection capabilities.
    • This approach enables more reliable autonomous docking of UAVs to outdoor charging stations, even in adverse wind conditions.
    • The proposed method offers a robust solution for improving the stability and precision of autonomous drone operations.