RBCC飞行器上升段燃耗近最优轨迹快速规划

doi:10.7527/S1000-6893.2025.31876

Abstract

Abstract:

To address the rapid trajectory planning problem for the ascent-phase of Rocket-Based Combined Cycle （RBCC） powered vehicles， this paper proposes a fuel-near-optimal trajectory planning method based on reduced-order model-derived fuel-optimal thrust approximation solutions and analytical altitude-velocity profile tracking. First， the ascent dynamics model is reduced through singular perturbation theory and energy-state approximation， deriving fuel-optimal mode switching equations and thrust approximation solutions. Subsequently， based on flight mechanism analysis， a three-segment analytical altitude-velocity profile is designed with constraint enforcement methods. Considering trajectory optimality， the fuel-optimal mode switching equations are employed as basis for segment parameter determination， developing a sequential iterative strategy for multi-segment profile parameter calculation to satisfy terminal altitude and velocity constraints. Finally， a bisection-based closed-loop correction algorithm is designed to meet terminal flight path angle constraints， thereby achieving rapid multi-constrained fuel-near-optimal ascent trajectory planning. Simulation studies on an RBCC vehicle validate the effectiveness， computational efficiency， and multi-mission adaptability of the method. Compared with conventional optimization methods， the proposed method maintains comparable fuel consumption while demonstrating superior computational efficiency.

Key words: RBCC vehicle, ascent-phase trajectory planning, near-optimal fuel consumption, modal switching equation, analytical design of standard profile

CLC Number:

V412

Xunliang YAN, Yuxuan YANG, Jiawei SHI, Peichen WANG. Rapid ascent-phase trajectory planning for near-optimal fuel consumption of RBCC vehicle[J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(18): 231876.

Figures/Tables 16

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Table 1

Simulation results of algorithm module functionality

方法	1	2	3
$h f / k m$	49.999 6	49.999 7	50.000 5
$M a f$	11.001 9	11.002 2	11.002 3
$θ f$ /（°）	24.939 5	24.523 0	24.616 0
求解耗时/s	0.99	0.18	0.17
最大动压/kPa	174.24	185.68	185.68
最大热流密度/（kW·m^-2）	795.64	794.98	826.73
最大过载	5.67	6.11	5.17

Table 1

Fig.7

Table 2

Fig.8

Table 3

Parameter setting for multi-task adaptability simulation

算例	$h f * / k m$	$M a f *$	$θ f *$ /（°）
1	50	11	23
2	50	11	17
3	54	11	23
4	50	10	23

Table 3

Table 4

Terminal deviation corresponding to different task conditions

算例	$Δ h f / m$	$Δ M a f$	$Δ θ f /$ （°）
1	0.489 4	0.002 4	0.095 5
2	0.641 2	0.001 8	0.006 9
3	0.876 9	0.001 7	0.003 8
4	0.449 9	0.001 4	0.015 4

Table 4

Fig.9

Table 5

Parameter setting for Monte Carlo simulation

参数	$3 σ$	参数	$3 σ$
$h 0$ /m	300	$h f$ /m	300
$V 0$ /（m·s^-1）	50	$V f$ /（m·s^-1）	50
$θ 0$ /（°）	0.2	$θ f$ /（°）	0.2

Table 5

Table 6

Statistical result of terminal deviation in Monte Carlo simulation

参数	$Δ h f / m$	$Δ M a f$	$Δ θ f /$ （°）
平均值	0.005 0	0.001 5	0.001 2
标准差	0.542 8	0.000 5	0.055 9

Table 6

Fig.10

References 23

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方法	本文方法	粒子群方法	hp自适应伪谱法	序列凸优化方法	传统剖面方法^［17］
终端高度/km	50.000 3	49.962 2	49.998 9	50.038 2	48.700 9
终端弹道倾角/（°）	20.025 6	19.905 9	19.996 1	20.103 1	20.136 6
最大动压/kPa	186.86	171.52	200.05	200.36	199.28
最大热流密度/（kW·m^-2）	673.69	675.50	675.53	671.97	726.81
最大过载	2.87	3.42	2.61	2.55	2.84
燃料消耗/kg	2 745.71	2 736.98	2 711.62	2 711.94	2 795.96
求解耗时/s	0.645 4	174.647 1	9.823 2	8.079 0	0.058 7

Rapid ascent-phase trajectory planning for near-optimal fuel consumption of RBCC vehicle

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