Entry Trajectory Optimization With Virtual Motion Camouflage Principle

This article proposes a bio-inspired algorithm called virtual motion camouflage (VMC) to reduce the dimension and computational cost of entry trajectory optimization problem for the reusable launch vehicle. In the VMC framework, the trajectory to be planned can be obtained by predefining the virtual prey motion according to the boundary conditions, then optimizing the reference point and path control parameters using sequential quadratic programming algorithm. Compared with Gauss pseudospectral method, due to the proposed approach reduce the number of discrete points and the equality constraints of the optimization model, it can improve the convergence speed and reduce computational cost effectively. Two minimum time entry examples, without and with no-fly-zone constraints, are presented to demonstrate the effectiveness of the proposed approach.