  Computational guidance is an emerging and accelerating trend in aerospace
guidance and control. Combining machine learning and convex optimization, this
paper presents a real-time computational guidance method for the
6-degrees-of-freedom powered landing guidance problem. The powered landing
guidance problem is formulated as an optimal control problem, which is then
transformed into a convex optimization problem. Instead of brutally using the
neural networks as the controller, we use neural networks to improve the
state-of-the-art sequential convex programming (SCP) algorithm. Based on the
deep neural network, an initial trajectory generator is designed to provide a
satisfactory initial guess for the SCP algorithm. Benefitting from designing
the initial trajectory generator as a sequence model predictor, the proposed
data-driven SCP architecture is capable of improving the performance of any
state-of-the-art SCP algorithm in various applications, not just powered
landing guidance. The simulation results show that the proposed method can
precisely guide the vehicle to the landing site. Moreover, through Monte Carlo
tests, the proposed method can averagely save 40.8% of the computation time
compared with the SCP method, while ensuring higher terminal states accuracy.
The proposed computational guidance scheme is suitable for real-time
applications.
