Hybrid controller of Lyapunov-based and nonlinear fuzzy-sliding mode for a quadrotor slung load system

Abstract In this paper, a new hybrid controller for a quadrotor slung load (QSL) system is presented. The QSL model structure has a complex and nonlinear system considering the effects of the swinging load on the quadrotor performance. However, most similar studies used linear controllers for the load position and attitude control. The lack of accurate data for the swinging load's moment of inertia and mass affects the linear controller bandwidth of the system position and increases the path tracking error, and therefore, undermines the results of such studies. In this study a new method is introduced for designing a nonlinear trajectory controller for the QSL system. The proposed method, sliding mode control (SMC), is based on the Lyapunov function and is optimized via an intelligent fuzzy-genetic algorithm. The controller consists of two control loops and one optimization loop. The inner-loop employs a Lyapunov-based controller capable of stabilizing the closed-loop system. The outer-loop is based on a nonlinear SMC and is designed to use system state variables prediction from the inner-loop system outcomes to improve the transient state parameters. The optimization loop can attain the fuzzy optimized function members coupled with SMC via a genetic algorithm. The illustrated simulation results substantiate the robustness and effectiveness of the proposed method used in eliminating the pendulum load fluctuations and their effect on the quadrotor state variables with a constant cable length.