论文标题

火星进入轨迹计划,范围离散化和连续的凸范围

Mars Entry Trajectory Planning with Range Discretization and Successive Convexification

论文作者

Liu, Xu, Li, Shuang, Xin, Ming

论文摘要

本文通过范围离散化开发了一种用于火星进入轨迹计划的顺序凸编程方法。为了提高数值集成的准确性,将进入轨迹的范围选择为自变量,而不是时间或能量。使用扩张因子将性能指数的输入动力学和集成间隔归一化,以便可以将困难的自由时间编程问题转换为固定最终范围优化问题。将相对于该范围的银行角度速率作为新的控制输入引入,以使控制与国家的控制并促进对银行角度及其速率上的约束的共同化。通过不等式松弛,非线性越南角度约束进一步放松成线性。此外,关于飞行时间作为状态变量,非凸的最小时间性能指数被传达。然后,线性化后,可以将火星进入轨迹计划问题提出到凸编程的框架中。通过范围离散化和连续的凸式化,重新计算的火星进入轨迹计划问题被转录为一系列凸优化的子问题,可以使用凸编程算法顺序解决。使用虚拟控制和自适应信任区域技术来提高算法的准确性,鲁棒性和计算效率。提出了与比较研究的数值模拟,以证明所提出的算法的收敛性能和效率。

This paper develops a sequential convex programming approach for Mars entry trajectory planning by range discretization. To improve the accuracy of numerical integration, the range of entry trajectory is selected as the independent variable rather than time or energy. A dilation factor is employed to normalize the entry dynamics and integration interval of the performance index so that the difficult free-final-time programming problem can be converted to a fixed-final-range optimization problem. The bank angle rate with respect to the range is introduced as the new control input in order to decouple the control from the state and facilitate convexification of constraints on the bank angle and its rate. The nonlinear bank angle rate constraint is further relaxed into a linear one via inequality relaxation. Moreover, the nonconvex minimum-time performance index is convexified by regarding flight time as a state variable. Then, the Mars entry trajectory planning problem can be formulated into the framework of convex programming after linearization. By range discretization and successive convexification, the reformulated Mars entry trajectory planning problem is transcribed into a series of convex optimization sub-problems that can be sequentially solved using the convex programming algorithm. The virtual control and adaptive trust-region techniques are employed to improve the accuracy, robustness, and computation efficiency of the algorithm. Numerical simulations with comparative studies are presented to demonstrate the convergence performance and efficiency of the proposed algorithm.

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