Abstract: In order to improve the computational efficiency of trajectory replanning in ascending flight of launch vehicles experiencing thrust drop fault, an online trajectory replanning method based on the intelligent decision making is proposed by transforming the original optimization problem into two independent problems, i.e. the intelligent decision making of the optimal rescue orbit and the optimization problem to achieve minimum fuel consumption (MFC). The “fault state rescue orbit” sample dataset is generated by offline solving the trajectory replanning problem with all kinds of different thrust drop fault states. Based on the sample dataset, the decision making model of the optimal rescue orbit is established by training a radial basis function neural network. The rescue orbit decision model is online used in actual flight to decide the optimal rescue orbit elements corresponding to the current thrust drop fault state. Subsequently, the MFC problem using the rescue orbit as the target orbit is solved. And so the optimal rescue trajectory of the launch vehicle under thrust drop failure is obtained online. The numerical simulations show that the proposed method improves the computational efficiency by more than two orders of magnitude and gives the same optimal rescue trajectory, compared with solving the original trajectory replanning problem directly.

Key words: Launch vehicle, Optimal rescue orbit, Online trajectory optimization, Intelligent decision making, Radial basis function neural network, Adaptive pseudospectral method