IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i4p1380-d749041.html
   My bibliography  Save this article

How Much Energy Do We Need to Fly with Greater Agility? Energy Consumption and Performance of an Attitude Stabilization Controller in a Quadcopter Drone: A Modified MPC vs. PID

Author

Listed:
  • Michał Okulski

    (Institute of Control and Computation Engineering, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665 Warsaw, Poland)

  • Maciej Ławryńczuk

    (Institute of Control and Computation Engineering, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665 Warsaw, Poland)

Abstract

Increasing demand for faster and more agile Unmanned Aerial Vehicles (UAVs, drones) is observed in many scenarios, including but not limited to medical supply or Search-and-Rescue (SAR) missions. Exceptional maneuverability is critical for robust obstacle avoidance during autonomous flights. A novel modification to the Model Predictive Controller (MPC) is proposed, which drastically improves the speed of the attitude controller of our quadcopter drone. The modified MPC is suitable for the onboard microcontroller and the 400 Hz main control loop. The peak and total energy consumption and the performance of the attitude controllers are assessed: the modified MPC and the default Proportional-Integral-Derivative (PID). The tests were conducted in a custom-implemented Flight Mode in the ArduCopter software stack, securing the drone in a test harness, which guarantees the experiments are repetitive. The ultimate MPC greatly increases maneuverability of the drone and may inspire more research related to faster obstacle avoidance and new types of hybrid attitude controllers to balance the agility and the power consumption.

Suggested Citation

  • Michał Okulski & Maciej Ławryńczuk, 2022. "How Much Energy Do We Need to Fly with Greater Agility? Energy Consumption and Performance of an Attitude Stabilization Controller in a Quadcopter Drone: A Modified MPC vs. PID," Energies, MDPI, vol. 15(4), pages 1-13, February.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1380-:d:749041
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/4/1380/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/4/1380/
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yanxiang Yang & Xiangyin Zhang & Jiayi Zhou & Bo Li & Kaiyu Qin, 2022. "Global Energy Consumption Optimization for UAV Swarm Topology Shaping," Energies, MDPI, vol. 15(7), pages 1-21, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1380-:d:749041. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.