Vol.68, No.2, 2021, pp.2265-2282, doi:10.32604/cmc.2021.017237
Grey Wolf Optimization Based Tuning of Terminal Sliding Mode Controllers for a Quadrotor
  • Rabii Fessi1, Hegazy Rezk2,3,*, Soufiene Bouallègue1,4
1 Research Laboratory in Automatic Control (LARA), National Engineering School of Tunis (ENIT), University of Tunis EL MANAR, Tunis, 1002, Tunisia
2 College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11911, Saudi Arabia
3 Department of Electrical Engineering, Faculty of Engineering, Minia University, Minia, 61517, Egypt
4 High Institute of Industrial Systems of Gabès (ISSIG), University of Gabès, Gabès, 6011, Tunisia
* Corresponding Author: Hegazy Rezk. Email:
Received 23 January 2021; Accepted 25 February 2021; Issue published 13 April 2021
The research on Unmanned Aerial Vehicles (UAV) has intensified considerably thanks to the recent growth in the fields of advanced automatic control, artificial intelligence, and miniaturization. In this paper, a Grey Wolf Optimization (GWO) algorithm is proposed and successfully applied to tune all effective parameters of Fast Terminal Sliding Mode (FTSM) controllers for a quadrotor UAV. A full control scheme is first established to deal with the coupled and underactuated dynamics of the drone. Controllers for altitude, attitude, and position dynamics become separately designed and tuned. To work around the repetitive and time-consuming trial-error-based procedures, all FTSM controllers’ parameters for only altitude and attitude dynamics are systematically tuned thanks to the proposed GWO metaheuristic. Such a hard and complex tuning task is formulated as a nonlinear optimization problem under operational constraints. The performance and robustness of the GWO-based control strategy are compared to those based on homologous metaheuristics and standard terminal sliding mode approaches. Numerical simulations are carried out to show the effectiveness and superiority of the proposed GWO-tuned FTSM controllers for the altitude and attitude dynamics’ stabilization and tracking. Nonparametric statistical analyses revealed that the GWO algorithm is more competitive with high performance in terms of fastness, non-premature convergence, and research exploration/ exploitation capabilities.
Quadrotor; cascade control; fast terminal sliding mode control; grey wolf optimizer; nonparametric Friedman analysis
Cite This Article
R. Fessi, H. Rezk and S. Bouallègue, "Grey wolf optimization based tuning of terminal sliding mode controllers for a quadrotor," Computers, Materials & Continua, vol. 68, no.2, pp. 2265–2282, 2021.
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