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Design and Real-Time Implementation of a Cascaded Model Predictive Control Architecture for Unmanned Aerial Vehicles

Author

Listed:
  • Patricio Borbolla-Burillo

    (Tecnologico de Monterrey, School of Engineering and Sciences, Ave. Eugenio Garza Sada 2501, Monterrey 64849, Mexico)

  • David Sotelo

    (Tecnologico de Monterrey, School of Engineering and Sciences, Ave. Eugenio Garza Sada 2501, Monterrey 64849, Mexico)

  • Michael Frye

    (Department of Engineering, University of the Incarnate Word, San Antonio, TX 78209, USA)

  • Luis E. Garza-Castañón

    (Tecnologico de Monterrey, School of Engineering and Sciences, Ave. Eugenio Garza Sada 2501, Monterrey 64849, Mexico)

  • Luis Juárez-Moreno

    (Tecnologico de Monterrey, School of Engineering and Sciences, Ave. Eugenio Garza Sada 2501, Monterrey 64849, Mexico)

  • Carlos Sotelo

    (Tecnologico de Monterrey, School of Engineering and Sciences, Ave. Eugenio Garza Sada 2501, Monterrey 64849, Mexico)

Abstract

Modeling and control are challenging in unmanned aerial vehicles, especially in quadrotors where there exists high coupling between the position and the orientation dynamics. In simulations, conventional control strategies such as the use of a proportional–integral–derivative (PID) controller under different configurations are typically employed due to their simplicity and ease of design. However, linear assumptions have to be made, which turns into poor performance for practical applications on unmanned aerial vehicles (UAVs). This paper designs and implements a hierarchical cascaded model predictive control (MPC) for three-dimensional trajectory tracking using a quadrotor platform. The overall system consists of two stages: the mission server and the commander stabilizer. Different from existing works, the heavy computational burden is managed by decomposing the overall MPC strategy into two different schemes. The first scheme controls the translational displacements while the second scheme regulates the rotational movements of the quadrotor. For validation, the performance of the proposed controller is compared against that of a proportional–integral–velocity (PIV) controller taken from the literature. Here, real-world experiments for tracking helicoidal and lemniscate trajectories are implemented, while for regulation, an extreme wind disturbance is applied. The experimental results show that the proposed controller outperforms the PIV controller, presenting less signal effort fluctuations, especially in terms of rejecting external wind disturbances.

Suggested Citation

  • Patricio Borbolla-Burillo & David Sotelo & Michael Frye & Luis E. Garza-Castañón & Luis Juárez-Moreno & Carlos Sotelo, 2024. "Design and Real-Time Implementation of a Cascaded Model Predictive Control Architecture for Unmanned Aerial Vehicles," Mathematics, MDPI, vol. 12(5), pages 1-20, February.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:5:p:739-:d:1349166
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    References listed on IDEAS

    as
    1. Xiaodong Zhang & Xiaoli Li & Kang Wang & Yanjun Lu, 2014. "A Survey of Modelling and Identification of Quadrotor Robot," Abstract and Applied Analysis, Hindawi, vol. 2014, pages 1-16, October.
    2. Alexander Nazin & Hussain Alazki & Alexander Poznyak, 2023. "Robust Tracking as Constrained Optimization by Uncertain Dynamic Plant: Mirror Descent Method and ASG—Version of Integral Sliding Mode Control," Mathematics, MDPI, vol. 11(19), pages 1-15, September.
    3. Kairui Chen & Zhangmou Zhu & Xianxian Zeng & Junwei Wang, 2023. "Distributed Observers for State Omniscience with Stochastic Communication Noises," Mathematics, MDPI, vol. 11(9), pages 1-14, April.
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