基于随机森林的月球表面软着陆实时最优控制

针对传统月球表面软着陆在处理入轨偏差或降落过程轨迹偏离实时性差，文章提出一种通过监督学习离线训练随机森林结构，使得着陆器在降落过程中根据其状态，通过训练的随机森林结构在线计算其控制量，从而达到实时控制的目的。文章还提出一种基于随机森林的模型对月球着陆过程轨迹重规划技术，通过动力学建模将月球着陆过程分成制动段、接近段和着陆段共3个阶段，利用离线训练好的模型根据航天器状态在线计算其控制量，并通过三段下降过程逐渐降低航天器位置速度误差。仿真结果表明，针对入轨偏离500m的情况，通过第一阶段将位置误差缩短至50m，保证了航天器状态位于第二阶段训练集内，经过接近段后再将位置误差缩小至10m范围内，速度误差降至0.01m/s量级，满足着陆误差要求，且控制量计算时间短，达到了轨迹实时重规划的目的。

Optimal real-time lunar soft landing using random forest

Traditional optimal trajectories for lunar soft landing can’t be calculated in real time if there’s error when spacecraft entering orbit or during the descending stage. A novel scheme was proposed to replan the trajectory in real time for the random forest model. The descending stage was modeled by three phases: deboost, descend and land. The modelcan predict the control by the state of spacecraft as the input, the lander descend slower and the error gets smaller. The simulation results show that with a 500m error in entering orbit the distances deceases to 50m after the deboost phase. This error is in the range of the training set of descend phases, so after that the position error is less than 10m and the velocity error is less than 0.01m/s, both in tolerance. Meanwhile this method is fast enough to satisfy the real time control.