基于MPSP算法的高速飞行器上升段制导研究

针对高速飞行器的上升段制导问题,提出了一种基于模型预测静态规划(Model Predictive Static Programming,MPSP)算法的自适应制导方法,实现了在存在不确定情况下高速飞行器对期望末端状态的高精度制导。MPSP算法在求解带末端约束的两点边值问题方面具有高效性,能够实现飞行过程中制导指令的快速计算。此外,考虑到MPSP算法是一种依赖于模型的算法,而复杂多变的大气环境带来了气动参数的不确定性。采用带遗忘因子的递归最小二乘法(Recursive Least Squares,RLS)在线地估计综合升力系数和综合阻力系数偏差,对模型进行偏差修正,提供了制导方案的自适应性。仿真结果表明,该制导方案能较好地完成飞行任务。

Ascent Guidance for High Speed Vehicles Based on MPSP

For the strong nonlinearity and uncertainty in the ascent guidance problem of vehicle,an adaptive ascent guidance scheme based on model predictive static programming(MPSP)algorithm is investigated which realizes the high guidance precision of the vehicle to the desired terminal state under the condition of uncertainty.MPSP algorithm is effective in solving the two-point boundary value problem with terminal constraints,the guidance command can be obtained fast.Besides,considering MPSP is a model-dependent algorithm that will be affected by the complex and changeable atmospheric environment,the recursive least squares(RLS)method with forgetting factor is adopted to estimate the comprehensive lift coefficient and comprehensive drag coefficient in a real flight condition,which can provide the adaptability of guidance scheme.Simulation results show that the proposed guidance scheme can achieve better performance in flight missions.