Chance-Constrained Trajectory Optimization for Mars Ascent Vehicle Based on Sequential Convex Optimization

The Mars ascent vehicle is tasked with sending Martian samples into space orbit, and its trajectory optimization is a crucial part of the Mars sample return mission. The scenario of the Mars ascent mission is complex and subject to various uncertainties. However, traditional trajectory optimizations are mostly based on nominal models, with limited ability to address uncertainties. In this thesis, the uncertainties in the initial position and thrust amplitude were described as chance constraints, so a chance-constrained trajectory optimization problem was established. Then the optimal trajectory was iteratively solved using sequential convex optimization method. Numerical simulation results verified the effectiveness of the proposed method. Comparisons also demonstrated that this method achieved better performance indicators and exhibited less conservatism than traditional robust optimization. In addition, a comparative analysis of the risk management effect of chance constraints and the conservatism of probability approximation functions was conducted.