Improved sequential convex programming using modified Chebyshev–Picard iteration for ascent trajectory optimization

Abstract

This paper presents an improved sequential convex programming (SCP) algorithm for ascent trajectory optimization of launch vehicles, by exploiting the state-of-the-art modified Chebyshev–Picard iteration (MCPI) technique. In the proposed algorithm, the MCPI technique is first utilized to transcribe the continuous-time optimization problem into a sequence of finite-dimensional subproblems. The lossless and successive convexification techniques are then employed to deal with the nonconvexity in optimization. The resulting convex subproblems can be reliably and efficiently solved via a primal-dual interior-point method solver. Numerical simulations for a minimum-time ascent trajectory optimization problem are conducted and the results show that the proposed algorithm has significant improvements over the standard SCP (which uses the Euler or trapezoidal rule for discretization), pseudospectral SCP, and GPOPS.