一种基于参数在线辨识的最优再入制导算法

An Optimal Reentry Profile Guidance Based on On-line Parameter Identification

为提高升力式飞行器再入飞行最优性及参数不确定条件下的制导精度,研究了一种基于参数在线辨识的最优高度-速度剖面跟踪制导算法。首先,采用分段三次多项式拟合飞行剖面,利用序列二次规划算法(SQP)优化获取最优分段点位置及飞行剖面系数,得到了一种满足总吸热量最小的高度-速度飞行剖面规划算法,并根据待飞航程与待飞航程预测值偏差确定是否进行重规划。其次,利用反馈线性化方法和航迹倾角偏差修正解算制导指令,并通过增广渐消记忆扩展卡尔曼滤波对不确定性参数在线辨识,利用辨识结果实现对制导指令的修正。最后,通过航迹偏角走廊控制倾侧角反号,实现侧向制导,完成跟踪制导算法设计。仿真结果表明,这种基于参数在线辨识的最优飞行剖面跟踪制导算法适应性强,计算速度快,具有较高的制导精度和一定的工程应用潜力。

Aiming at improving the optimality and the guidance precision of the lifting reentry vehicle with uncertain flight parameters,an optimal reentry guidance algorithm tracks height-velocity profile is proposed,which is based on on-line parameter identification. Firstly,an altitude-velocity reentry flight profile planning algorithm is proposed to minimize the total heat absorption,which fits the reference flight profile by piecewise cubic function,and the optimal section point position and reference profile parameter are obtained by using seqential quadratic programming( SQP) optimizing method,and re-profiling is determined according to the voyage deviation. Secondly,the guidance command is calculated by adopting the feedback linearization and flight path angle deviation feedback. In order to revise the guidance command,the uncertain disturbing parameters are estimated on-line by the augmented exponential fading factor extend Kalman filter. Lastly,the lateral guidance is accomplished through reversing the sign of the angle of bank. The simulation results demonstrate that the profile planning and guidance algorithm based on on-line parameter identification has strong adaptability,high computational efficiency,high guidance accuracy and high engineering application potential.