Feasible Sequential Convex Programming With Inexact Restoration for Multistage Ascent Trajectory Optimization

This article presents a feasible sequential convex programming method to solve the multistage ascent trajectory optimization problem. The proposed method is based on the inexact restoration technique, in which a more feasible intermediate iterate is first produced by solving a constrained least-squares problem, and then a more optimal iterate is generated by solving a convex programming problem constructed around the newly found feasible solution. By virtue of the inexact restoration idea, the proposed method prevents the common artificial infeasibility issue and can provide the intermediate iterate as a feasible suboptimal solution if the algorithmic procedure terminates before convergence. In addition, the Picard iteration-based convexification and Chebyshev polynomial-based discretization methods are employed in the proposed method, given their benefits in terms of robustness, efficiency, and solution accuracy. Numerical simulations for a minimum-time launch ascent problem are conducted, and the results show that the proposed method exhibits better practical performance than other sequential convex programming methods.