High-efficiency Reduced Sequential Convex Programming for Mars Ascent Vehicle Multi-phase Trajectory Optimization
- Paper ID
99111
- DOI
- author
- company
Northwestern Polytechnical University; Xi'an Modern Control Technology Research Institute; Northwestern Polytechnical University，NPU
- country
China
- year
2025
- abstract
The two-stage solid ascent vehicle is an important part of the large-scale Mars sample recovery program, and real-time trajectory optimization is crucial to enhance the autonomy and reliability of the Mars ascent vehicle. However, solving such a highly nonlinear and constrained optimization problem in real time is not trivial. Both indirect and direct methods have been used to address the mars ascent trajectory optimization problems. Indirect methods are intrinsically accurate and efficient, but frequently suffer from high sensitivity to the initial unknowns. Auxiliary homotopic techniques can help to improve the convergence behaviour but correspondingly increase the computational burden. In contrast, convex optimization, as a direct method, has been widely applied in aerospace trajectory optimization due to its good convergence property and high computational efficiency. Even for convex formulations, the size of an optimization problem directly impacts its computational time consumption. A lower number of unknowns and constraints naturally implies a smaller-size and thus easier-to-solve optimization problem. To address the problems of the optimal thrust design and the depleted shutdown guidance with multi-constraints for solid Mars ascent vehicle in sample return missions, an adaptive sequential convex programming is presented for rapid ascent trajectory optimization of Mars ascent vehicle with multiple flight phases. Firstly, the continuous-time dynamic equation is discretized based on the modified second-order Picard iteration formulation and the Chebyshev polynomial, so as to improve the computational efficiency of sequential convex programming by explicitly eliminating state variables and state equations from the optimization formulation. Secondly, the orbiting problem that considers terminal constraints is transformed into a nonlinear least-squares problem with respect to the terminal constraint function and solved with the Gauss-Newton method. 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. Finally, a comparison with the existing optimization methods and GPOPS optimization method verifies the optimality, correctness of the proposed method. The optimal thrust scheme of Mars ascent vehicles is also given. The numerical simulation results demonstrate that the MAV can enter the target orbit with high accuracy in the depleted shutdown mode with the interference of deviation and uncertainties by the proposed method. It has certain theoretical significance and engineering application value.