This article addresses the high‐accuracy intelligent trajectory tracking control problem of a quadrotor unmanned aerial vehicle (UAV) subject to external disturbances. The tracking error systems are first reestablished by utilizing the feedforward control technique to compensate for the raw error dynamics of the quadrotor UAV. Then, two novel appointed‐fixed‐time observers are designed for the processed error systems to reconstruct the disturbance forces and torques, respectively. And the observation errors can converge to origin within the appointed time defined by users or designers. Subsequently, two novel control policies are developed utilizing reinforcement learning methodology, which can balance the control cost and control performance. Meanwhile, two critic neural networks are used to replace the traditional actor‐critic networks for approximating the solutions of Hamilton–Jacobi–Bellman equations. More specifically, two novel weight update laws are developed. They can not only update the weights of the critic neural networks online, but also avoid utilizing the persistent excitation condition innovatively. And that the ultimately uniformly bounded stability of the whole control system is proved according to Lyapunov method by utilizing the proposed reinforcement learning‐based control polices. Finally, simulation results are presented to illustrate the effectiveness and superior performances of the developed control scheme.

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This paper investigates the problem of trajectory tracking control for quadrotor unmanned aerial vehicle (UAV) in the presence of dynamic obstacles and external disturbance forces/torques. More specifically, two new sliding mode disturbance observers are firstly designed to estimate the external disturbances, in which the observation errors can converge to zero in finite time. Furthermore, utilizing the observation information, a new sliding mode surface-like variable-based position tracking control scheme and a novel nonsingular terminal sliding mode-based attitude synchronization control scheme are developed to drive the UAV tracking the reference trajectory with obstacle avoiding. Moreover, the tracking errors of the close-loop control system can converge to zero within finite time by the analyses of Lyapunov methodology. Finally, the numerical simulation results are presented to illustrate the effectiveness of the proposed control schemes.

The attitude tracking control problem of rigid spacecraft subjected to external disturbance and prescribed performance is studied. A fast fixed‐time stability theorem is firstly developed. A continuous disturbance observer is then designed with its estimation error can be driven into a tiny neighborhood containing zero within fixed time. Moreover, the spacecraft attitude tracking error constrained by prescribed performance function is transformed into an unconstrained dynamics. A disturbance observer‐based prescribed performance tracking controller is designed to stabilize the unconstrained dynamics within fixed time. It also means that the attitude tracking errors meet the prescribed performance as the stabilization of the unconstrained dynamics. Spacecraft simulation examples are finally given to validate the effectiveness of the proposed controller.