Fuel-Optimal Low-Thrust Trajectory Optimization Using Indirect Method and Successive Convex Programming

Abstract

Although the robustness of indirect methods is enhanced by the homotopic approach and switching detection technique when applied to fuel-optimal low-thrust trajectory optimization, the bottleneck in adjoint initialization still needs further investigation. This paper overcomes this bottleneck by the adjoint mapping between the Lagrange multipliers of direct methods to the adjoint variables. The nonconvex optimization problem deduced from direct methods is converted into a convex one by lossless convexification and successive convex programming. By combining these techniques, a framework is built to effectively solve the fuel-optimal low-thrust trajectory optimization problem.