A deep neural network assisted trajectory optimization algorithm for vertical landing vehicles
It is challenging to solve the powered descent guidance problem online for its computational cost and uncertain initial conditions.An Hp-pseudospectral convex optimization algorithm assisted by deep neural network is presented.For the highly nonlinear dynamics in atmosphere,it is proved for the first time that the thrust magnitude profile has the Bang-Bang feature based on variational method and Pontryagin's maximum principle.In the wide range of initial states,the deep neural network is applied to learn the segment feature of optimal thrust offline.Then the trained neural net is embedded in the online successive convex optimization algorithm,which combines the Hp-pseudospectral discretization with Bang-Bang feature.This learning assisted strategy leads to more accurate results with the same number of discretized nodes.Numerical simulations show that the proposed algorithm shows better computational efficiency and adaptability to initial conditions.
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