Robust reinforcement learning control for quadrotor with input delay and uncertainties

In this paper, a new control method is proposed for the control of the quadrotor with timevarying input delay and uncertainties. The controller is primarily composed of two components: the reinforcement learning (RL) component and the robust component. The robust component is designed to ensure basic control performance. The basic control performance can guarantee the tracking characteristics with nonlinear uncertainties. The tracking error can be arbitrarily small by robust component. The RL component further improves the convergence speed and control precision on the basis of the basic control performance. Both components have input delay problems, and each component of the proposed method solves this problem separately. For the robust component, the filter is used to eliminate the uncertainties and restrain the influence of the input delay. It can ensure safety during the initial training period. For the RL component, the extended state method and the extending control period method are used to handle the problem of input delay with lower computational complexity. In addition, the convergence of network weights is proven, and the stability of the whole system is analyzed according to the Lyapunov method. Finally, simulation results are presented to illustrate the effectiveness and superior performance of our method.