Adaptive Sequential Convex Programming for Mars Ascent Vehicle Multiphase Trajectory Optimization

In this article, an adaptive sequential convex programming (SCP) is presented for rapid ascent trajectory optimization of Mars ascent vehicle with multiple flight phases. A modified Chebyshev–Picard iteration algorithm with error feedback integral quasi-linearization in the form of a second-order system is used to deal with the dynamic hard constraints in the optimal control problem, so as to improve the convergence performance of the SCP. Then, the adaptive trust-region strategy and the adaptive node number strategy are combined to further improve the computational speed in the case of undesirable initial guess. Numerical simulations of the Mars ascent problem are given to show that this method has superior performance in terms of convergence and computational time compared to existing optimization methods.