期刊
ALEXANDRIA ENGINEERING JOURNAL
卷 68, 期 -, 页码 551-560出版社
ELSEVIER
DOI: 10.1016/j.aej.2023.01.050
关键词
Unmanned aerial vehicles (UAVs); Nonlinear Backstepping Con-troller (NBC); Particle Swarm Optimization algorithm (PSO); Lyapunov Theory
A backstepping control strategy is proposed for an underactuated quadrotor slung load system to address the trajectory tracking issue for a cable-suspended load. The controller is designed using Lya-punov theory and backstepping technique to ensure asymptotic stability. The optimal set of parameters for the controller is determined using the Nonlinear Backstepping Controller using Particle Swarm Optimization algorithm (NBC-PSO). Simulation results demonstrate that the proposed NBC-PSO achieves approximately 14% improvement in load position trajectory convergence compared to the conventional NBC.
A backstepping control strategy for an underactuated quadrotor slung load system is presented in this paper. The issue of trajectory tracking for a cable-suspended load is addressed. A rigid body and a point mass are used to model the Quadrotor and slung-load, respectively. Lya-punov theory and backstepping technique are used to design the controller. Thrust and angular velocity control laws are carefully designed to ensure that the closed-loop system is asymptotically stable. The problem of finding the optimal set of parameters for the backstepping controller gains is formulated as an optimization problem and solved with the Nonlinear Backstepping Controller using Particle Swarm Optimization algorithm (NBC-PSO). The effectiveness of the optimized con-troller is established by simulation results done utilizing MATLAB/Simulink and compared with a conventional Nonlinear Backstepping Controller (NBC). It was observed that, the proposed NBC-PSO achieves approximately 14% improvement in load position trajectory convergence to the desired trajectory. The results confirm that the NBC-PSO controller is effective, and the controller converges faster than other controllers. (c) 2023 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).
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