Reinforcement learning-based adaptive spiral-diving Manoeuver guidance method for reentry vehicles subject to unknown disturbances

This paper proposed a reinforcement learning-based adaptive guidance method for a class of spiral-diving manoeuver guidance problems of reentry vehicles subject to unknown disturbances. First, the desired proportional navigation guidance law is designed for the vehicle based on the initial conditions, terminal constraints and the curve involute principle. Then, the first-order multivariable nonlinear guidance command tracking model considering unknown disturbances is established. And the controller design problem caused by the coupling of control variables is overcome by introducing the coordinate transformation technique. Moreover, the actor-critic networks and corresponding adaptive weight update laws are designed to cope with unknown disturbances. With the help of Lyapunov direct method, the stability of the system is proved. Subsequently, the range values of the guidance parameters are analysed. Finally, the validity as well as superiority of the proposed method are verified by numerical simulations.