IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i19p3510-d925700.html
   My bibliography  Save this article

An Improvement of Model Predictive for Aircraft Longitudinal Flight Control Based on Intelligent Technique

Author

Listed:
  • Mohamed El-Sayed M. Essa

    (Electrical Power and Machines Department, Institute of Aviation Engineering and Technology (I.A.E.T), Egyptian Aviation Academy, Imbab Airport, Giza 12815, Egypt)

  • Mahmoud Elsisi

    (Department of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung City 807618, Taiwan
    Department of Electrical Engineering, Faculty of Engineering (Shoubra), Benha University, 108 Shoubra St., B.O. Box 11629, Cairo 13511, Egypt)

  • Mohamed Saleh Elsayed

    (Aeronautical Engineering Department, Faculty of Engineering, Zagazig University, Zagazig 44519, Egypt)

  • Mohamed Fawzy Ahmed

    (Electrical Engineering Department (Control Engineering), Faculty of Engineering, Modern University for Technology & Information, Cairo 4410240, Egypt)

  • Ahmed M. Elshafeey

    (Electronics and Communication Department, Institute of Aviation Engineering and Technology (I.A.E.T), Egyptian Aviation Academy, Imbab Airport, Giza 12815, Egypt)

Abstract

This paper introduces a new intelligent tuning for the model predictive control (MPC) based on an effective intelligent algorithm named the bat-inspired algorithm (BIA) for the aircraft longitudinal flight. The tuning of MPC horizon parameters represents the main challenge to adjust the system performance. So, the BIA algorithm is intended to overcome the tuning issue of MPC parameters due to conventional methods, such as trial and error or designer experience. The BIA is adopted to explore the best parameters of MPC based on the minimization of various time domain objective functions. The suggested aircraft model takes into account the aircraft dynamics and constraints. The nonlinear dynamics of aircraft, gust disturbance, parameters uncertainty and environment variations are considered the main issues against the control of aircraft to provide a good flight performance. The nonlinear autoregressive moving average (NARMA-L2) controller and proportional integral (PI) controller are suggested for aircraft control in order to evaluate the effectiveness of the proposed MPC based on BIA. The proposed MPC based on BIA and suggested controllers are evaluated against various criteria and functions to prove the effectiveness of MPC based on BIA. The results confirm that the accomplishment of the suggested BIA-based MPC is outstanding to the NARMA-L2 and traditional PI controllers according to the cross-correlation criteria, integral time absolute error (ITAE), system overshoot, response settling time, and system robustness.

Suggested Citation

  • Mohamed El-Sayed M. Essa & Mahmoud Elsisi & Mohamed Saleh Elsayed & Mohamed Fawzy Ahmed & Ahmed M. Elshafeey, 2022. "An Improvement of Model Predictive for Aircraft Longitudinal Flight Control Based on Intelligent Technique," Mathematics, MDPI, vol. 10(19), pages 1-24, September.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:19:p:3510-:d:925700
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/19/3510/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/19/3510/
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Weijun Hu & Jiale Quan & Xianlong Ma & Mostafa M. Salah & Ahmed Shaker, 2023. "Analytical Design of Optimal Model Predictive Control and Its Application in Small-Scale Helicopters," Mathematics, MDPI, vol. 11(8), pages 1-15, April.
    2. Denis Sidorov, 2023. "Preface to “Model Predictive Control and Optimization for Cyber-Physical Systems”," Mathematics, MDPI, vol. 11(4), pages 1-3, February.

    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:jmathe:v:10:y:2022:i:19:p:3510-:d:925700. 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.