Adaptive Tracking Control of Quadrotor Dragging System with Variable-Mass Surface Payload

In this paper., an adaptive sliding mode control method based on nonlinear disturbance observer (NDO) and radial basis function neural network (RBFNN) is proposed for a class of quadrotor using cable to drag surface payload with variable mass under unknown time-varying external disturbances and model parameter uncertainties. Firstly, a coupled position dynamics model is established. Secondly, an adaptive sliding mode controller is designed to ensure the convergence of tracking errors. The universal approximation ability of RBFNN and NDO are used to estimate and compensate the model's nonlinear coupling component and the unknown time-varying external disturbances, respectively. In addition, adaptive laws for network weights and system total mass are provided for online updating and compensation. Finally, the effectiveness of the proposed control method is verified by theoretical analysis and numerical simulation.