Adaptive Predefined-Time Tracking Control of Quadrotor Unmanned Aerial Vehicle Based on Sliding Mode Technique and RBF NN

The paper focuses on the adaptive predefined-time tracking control problem of quadrotor unmanned aerial vehicle (UAV) by using sliding mode technique integrated with radial basis function neural network (RBF NN). A sliding mode variable is suitably introduced in the predefined-time setting, and the RBF NN is efficiently utilized to estimate the online unknown dynamics of quadrotor UAV. Furthermore, the convergence proof of the closed-loop control system is rigorously performed from mathematically, which can fully ensure the stability, adaptability, and robustness of the developed tracking control scheme. Subsequently, the feasibility and effectiveness of the proposed control method are verified by numerical simulations.