使用序列凸规划的太阳帆深空轨迹优化
Solar-Sail Deep Space Trajectory Optimization Using Successive Convex Programming
AI总结：
本文提出基于序列凸规划的行星际太阳帆任务时间最优轨迹优化方法，通过变量替换和序列线性化等凸化技术转化非凸问题，并引入增广目标函数确保收敛，数值结果验证了算法的有效性与准确性。
摘要：
本文提出了一种使用序列凸规划求解行星际太阳帆任务时间最优轨迹优化问题的新方法。基于该非凸问题，讨论了变量替换、序列线性化、信赖域和虚拟控制等不同凸化技术，以将原问题转化为序列凸规划形式。由于终末时间自由，对非凸终末状态约束进行迭代序列线性化。凸化过程后，每个子问题都变为可有效求解的凸问题。引入增广目标函数以确保算法的收敛性能和有效性。随后，设计了在序列求解过程中求解离散子问题的算法。最后，数值结果证明了算法的有效性和准确性。
英文摘要：
This paper presents a novel methodology for solving the time-optimal trajectory optimization problem for interplanetary solar-sail missions using successive convex programming. Based on the non-convex problem, different convexification technologies, such as change of variables, successive linearization, trust regions and virtual control, are discussed to convert the original problem into the formulation of successive convex programming. Because of the free final-time, successive linearization is performed iteratively for the nonconvex terminal state constraints. After the convexification process, each of problems becomes a convex problem, which can be solved effectively. An augmented objective function is introduced to ensure the convergence performance and effectiveness of our algorithm. After that, algorithms are designed to solve the discrete sub-problems in a successive solution procedure. Finally, numerical results demonstrate the effectiveness and accuracy of our algorithms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
