With the increase of the interest in solar sailing, it is required to provide a basis for future detailed planetary escape mission analysis by drawing together prior work, clarifying and explaining previously anomalies. In this paper, a technique for escaping the Earth by using a solar sail is developed and numerically simulated. The spacecraft is initially in a geosynchronous transfer orbit (GTO). Blended solar sail analytical control law, explicitly independent of time, are then presented, which provide near-optimal escape trajectories and maintain a safe minimum altitude and which are suitable as a potential autonomous onboard controller. This control law is investigated from a range of initial conditions and is shown to maintain the optimality previously demonstrated by the use of a single-energy gain control law but without the risk of planetary collision. Finally, it is shown that the blending solar sail analytical control law is suitable for solar sail on-board autonomously control system.
Although the shape-based method has been proven to be useful for low-thrust trajectory design,and be capable to provide near-optimal solution for a more accurate trajectory optimization method,it is slightly non-effective when used in some 3D cases.In this paper,a modified 3D shape-based method is proposed for earth trajectory design.In this approach,in consideration of the sinusoidal periodic variation in z direction of actual trajectory,a new exponential sinusoid model is chosen for the out-of-plane motion,with four coefficients such that four scalar out-of-plane boundary conditions can be satisfied.After deriving the 3D shape-based procedure,low-thrust trajectory design example with modest inclination change is given.The results demonstrate that this modified approach is feasible for the transfer trajectory design,and comparing to the former shape-based method,the z direction solution is more coincident with the actual situation,furthermore,the solution may be used for further mission planning,trajectory evaluation and optimization.
