Optimal trajectory-tracking guidance for reusable launch vehicle based on adaptive dynamic programming

An optimal trajectory-tracking guidance method for Reusable Launch Vehicle (RLV) based on neural Adaptive Dynamic Programming (ADP) is proposed. Firstly, a reentry reference trajectory and the corresponding steady-state control are generated based on the Gauss Pseudo-spectral Method (GPM) with saturation constraints on the amplitude, rate and acceleration of flow angles. A single-critic ADP controller is designed for optimal feedback control, which is combined with the steady-state control to realize the trajectory-tracking guidance. An innovative weight iteration algorithm for the critic neural network is proposed to reduce training computation and improve guidance accuracy. Simulation results show that with initial state errors and system uncertainty, the terminal position error is within 2.1 km in a reentry flight whose range covers more than 5500 km. Involved with the innovative weight iteration method, the training computation is reduced by 95% compared with the traditional gradient descent method and the guidance performance is also improved.