月球探测器动力下降段最优轨迹参数化方法

为保证月球探测器进入姿态调整段时具有充分的高度与速度余量,本文提出一种基于控制变量参数化的月球探测器动力下降段最优轨迹求解方法。在三维探测器软着陆动力学模型基础上,将月球探测器软着陆制导律设计等效为燃料最优约束下的探测器俯仰角控制问题,利用控制变量参数化(Control Variables Parameterization, CVP)方法将该控制问题中的控制变量与约束条件转化为非线性规划问题求解,并引入时间尺度变换,将着陆时间序列加入待规划参数,进而求得满足精度的最优数值解。蒙特卡罗仿真实验表明,与传统的显式制导律相比,本文提出的参数化制导方法在动力下降段燃料更省,动力下降段的起始高度在±20%范围内波动时,仍能以高精度速度和高度指标完成末制导。

Control Variables Parameterization Method of Powered Descent Trajectory for Lunar Explorer

In this paper, considering the sufficient height and velocity margin of a lunar explorer entering the attitude adjustment phase, an improved polynomial guidance law is proposed, which is solved by the control variables parameterization method in the task of achieving the powered descent phase of soft landing of a lunar explorer. Firstly, the dynamics model of the lunar explorer is described in a three dimensional coordinate system. Secondly, we transform the guidance law for the landing guidance problem into the minimum fuel problem of the optimal vertical attitude angle control problem. Then, we use the control vector parameterization method to solve the optimal control problem, in which we formulate the constraints and the control vector into the nonlinear programming method as the planning parameters, furthermore, the time scaling method to split up the time, to get the precise numeric results. The Monte Carlo simulation experiments show that the guidance method proposed in this paper can accomplish the soft landing of the lunar explorer, compared with the traditional polynomial law, in less fuel consumption. While changing the initial height within ±20% before the powered down phase of the lunar explorer in the simulation, the algorithm proposed can still satisfy the soft landing task with high precision speed and height index.