一种基于在线能量推演的自适应末端能量管理方法

An Effective and Adaptive Method of Terminal Area Energy Management(TAEM) Based on Online Energy Backstepping

新一代RLV要求末端能量管理(TAEM)可适应较大的窗口能量偏差,且BTT模式使飞行器的纵向和横侧向耦合严重,高度-速度难以协调控制。针对此问题,文章提出了一种基于在线能量推演的强自适应TAEM方法。该方法的"直接进入柱面可变"能量管理模式利用能量推演计算手段在线生成期望的能量-航程剖面,基于该剖面在线规划飞行轨迹和航向校正圆,使TAEM轨迹自适应窗口能量的不确定性,且易于实现高度速度协调控制。随后将弹道跟踪问题转化为模型预测静态规划(MPSP)问题,基于优化理论设计了在线迭代自适应制导律,解决了该模式在扰动条件下的轨迹自适应跟踪问题。3DOF仿真表明:该方法具有很好的快速在线能力和末端精度,在TAEM窗口能量扰动±25%的范围内均能保证飞行器安全进入自动着陆窗口,末端速度偏差小于10 m/s,高度偏差小于350 m,横向偏差小于150 m。

The initial energy uncertainty of TAEM＇ s window and its altitude-velocity coordinated control have drawn more and more attention, but most of the available research results are, in our opinion, not effective enough to solve these problems. We propose what we believe to be an effective and adaptive TAEM method based on online energy backstepping. Sections 1 and 2 explain the adaptive energy management mode and then design the adaptive guidance law. Section 1 develops an adaptive energy management mode, namely the direct entry and cone-alterable mode, which predicts the trajectory range with energy backstepping by using eq. （ 11 ）, and then plans the trajectory with eqs. （12） through （16） dynamically; Figs. 1 through 4 are worth paying special attention to. Section 2 transforms a trajectory tracking problem into a model predictive static programming problem with eq. （20）, and then uses the optimal theory to design an easily online adaptive guidance law by using eqs. （21） through （31）. Section 3 gives a simulation example; the simulation results, presented in Table 1 and Fig. 6, which includes four sub-figures, show preliminarily that, on the condition that the TAEM window＇ s disturbance is ± 25 %, an aircraft is able to enter into the auto-landing phase safely with the terminal velocity error of less than 10 m/s, altitude error of less than 350 m, lateral distance error of less than 150 m, demonstrating that the our method is indeed effective and has a better precision and online adaptiveness.