基于神经网络与分数阶滑模的行星进入段轨迹跟踪控制

Trajectory Tracking Control of Planetary Entry Phase Based on Neural Network and Fractional Sliding Mode

针对行星探测器进入段着陆过程中存在干扰影响着陆精度的问题,提出一种基于径向基(Radial Basis Function,RBF)神经网络的分数阶滑模控制方法。基于滑模控制设计探测器进入段轨迹跟踪控制方法,引入分数阶微积分缓解滑模控制产生的抖振,利用RBF神经网络对大气密度不确定干扰进行估计补偿,将该方法应用于火星着陆场景仿真。仿真结果表明:该控制方法能够在未知大气密度不确定干扰下,对探测器着陆轨迹进行精确跟踪,使得行星探测器高精度的到达开伞点,实现行星探测器稳定着陆。

A fractional order sliding mode control method based on Radial Basis Function(RBF)neural network is proposed to solve the landing accuracy being affected by the interference during the landing process of planetary probe.Based on sliding mode control,a trajectory tracking control method for the entry phase of the probe is designed.Fractional calculus is introduced to alleviate the chattering caused by sliding mode control.RBF neural network is used to estimate and compensate the atmospheric density uncertainty.The method is applied to Mars landing scene simulation.The simulation results show that the proposed control method can accurately track the landing trajectory of the probe under the interference of unknown atmospheric density and uncertainty,so that the planetary probe can reach the parachute opening point with high accuracy and achieve stable landing of the planetary probe.