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Development of Model Predictive Controller for a Tail-Sitter VTOL UAV in Hover Flight.

Boyang Li1, Weifeng Zhou2, Jingxuan Sun3

  • 1Department of Mechanical Engineering, The Hong Kong Polytechnic University, Hong Kong, China. boyang.li@connect.polyu.hk.

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|September 12, 2018
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Summary

This study introduces a model predictive controller (MPC) for controlling vertical take-off and landing (VTOL) tail-sitter unmanned aerial vehicles (UAVs). The developed MPC ensures robust position holding and trajectory tracking for UAVs, even in windy conditions.

Keywords:
flight experimenthardware-in-loop (HIL) simulationmodel predictive control (MPC)tail-sitterunmanned aerial vehicles (UAV)vertical takeoff and landing (VTOL)

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Area of Science:

  • Aerospace Engineering
  • Control Systems
  • Robotics

Background:

  • Vertical Take-Off and Landing (VTOL) tail-sitter unmanned aerial vehicles (UAVs) offer versatile flight capabilities.
  • Precise position control is critical for UAVs, especially during hover and in challenging environmental conditions.
  • Existing control strategies may struggle with disturbances like wind gusts.

Purpose of the Study:

  • To develop and validate a model predictive controller (MPC) for precise position control of a VTOL tail-sitter UAV.
  • To enhance the UAV's ability to reject measured and unmeasured disturbances.
  • To demonstrate the controller's effectiveness in both simulation and real-world flight scenarios.

Main Methods:

  • A six-degree-of-freedom (DOF) nonlinear dynamic model of a quad-rotor tail-sitter UAV was created using wind tunnel data.
  • An augmented linearized state-space model was used to develop the model predictive position controller.
  • Disturbance models were integrated into the control design for improved robustness.
  • Hardware-in-the-loop (HIL) simulations and real-time indoor experiments were conducted for verification and tuning.

Main Results:

  • The model predictive controller demonstrated effective trajectory tracking performance.
  • The controller exhibited robust position holding capabilities.
  • The system showed resilience to prevailing and gusty wind conditions.

Conclusions:

  • The proposed model predictive controller is a viable solution for precise position control of VTOL tail-sitter UAVs.
  • The controller's ability to handle disturbances enhances its practical applicability.
  • Successful HIL and real-time experiments validate the controller's performance in dynamic environments.