Adaptive Neural Predefined-Time Attitude Control of an Uncertain Quadrotor UAV With Actuator Fault

Abstract

This brief addresses the attitude stabilization problem of unmanned aerial vehicles (UAVs) like quadrotors with uncertain inertia, external disturbances, and actuator faults simultaneously in predefined time. The adaptive predefined-time sliding mode control (SMC) incorporated with a radial basis function neural network (RBFNN) is designed to track the desired trajectory and estimate the uncertainty of the system effectively to enhance the control performance. The proposed control strategy utilizes the sliding manifold, which ensures state convergence in a predefined time. The settling time of the presented control scheme can be arbitrarily chosen in advance compared to the traditional fixed-time and finite-time control strategies. The boundedness of the complete system is verified using Lyapunov stability theory. Finally, comparative results are presented to demonstrate the effectiveness of the proposed control scheme.