Picard-Chebyshev-Based Improved Sequential Convexification Method for Reentry Trajectory Planning

In this paper, an improved sequential convexification method is proposed to solve efficiently the highly nonlinear reentry trajectory planning problem of high speed vehicles based on the Picard-Chebyshev iteration technique, which enhances the robustness of the proposed algorithm to initialization. Firstly, the Picard-Chebyshev-based discretization method is utilized to convert the continuous differential dynamic equation into a discrete algebra constraint. Then, based on the augmented Lagrange function method and relaxation technique, the original problem is transformed to a series of augmented-Lagrange-function-formed slack convex subproblems and iteratively solved by the professional convex problem solver. Numerical simulation results show that the proposed method is effective and efficient, and possesses strong robustness to the initial guess.