Reinforcement learning-based tracking control for a quadrotor unmanned aerial vehicle under external disturbances

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.