This paper investigates the fixed-time tracking control problem of an unmanned aerial vehicle (UAV) considering the disturbance, input saturation, and actuator failure. According to the hierarchical control principle, the UAV dynamics are decomposed into a translational and rotational loop to accommodate the controller design. A novel nonsingular fixed-time backstepping controller based on switching variables is proposed to achieve fast convergence of system tracking errors within a fixed time. To overcome the effect of the disturbance and the actuator failure, two fixed-time disturbance observers are designed in two loops, respectively. By integrating the fixed-time auxiliary variables into the dynamic controllers, the problem of input saturation can be addressed. In addition, the tracking errors of the closed-loop system converge to the neighborhood of the origin in a fixed time. Finally, sufficient simulation results verify the validity of the proposed control framework for the UAV.

This paper proposes a multi-stage dueling deep Q-network (MS-DDQN) algorithm to address the high-speed aerial vehicle evasion problem. High-speed aerial vehicle pursuit and evasion are an ongoing game attracting significant research attention in the field of autonomous aerial vehicle decision making. However, traditional maneuvering methods are usually not applicable in high-speed scenarios. Independent of the aerial vehicle model, the implemented MS-DDQN-based method searches for an approximate optimal maneuvering policy by iteratively interacting with the environment. Furthermore, the multi-stage learning mechanism was introduced to improve the training data quality. Simulation experiments were conducted to compare the proposed method with several typical evasion maneuvering policies and to reveal the effectiveness and robustness of the proposed MS-DDQN algorithm.