基于可达集与迭代学习的栖落机动轨迹控制方法

doi:10.7527/S1000-6893.2024.31227

Abstract

Abstract:

The perch maneuver of an aircraft requires high-angle-of-attack flight， rapid deceleration， and precise landing in a target area， involving complex nonlinear dynamics and fast time-varying characteristics. To address the challenges posed by these dynamic complexities and stringent control performance demands， this paper proposes a trajectory control design method based on reachability sets and iterative learning. First， an improved backward reachability set computation algorithm is developed for the perch maneuver control system. Then， an iterative learning trajectory control method is introduced， guided by the reachability set to ensure convergence. This method begins with an inaccurate initial perch trajectory， and iteratively refines the trajectory and optimizes controller parameters using the reachability set of each iteration to guide the next. After several learning iterations， the aircraft can successfully perform the perch maneuver while meeting multiple constraints and accurately landing in the target area， with a large convergence domain guaranteed. Additionally， for cases where the perch maneuver model is unknown， a trajectory control method based on the SINDy identification algorithm is designed. Finally， simulation validation and comparison of the proposed reachability set algorithm and iterative learning trajectory control method are conducted. The results demonstrate that the improved reachability set algorithm more accurately captures the reachability set of the nonlinear perch model， and the designed trajectory control method can quickly learn and achieve successful perch maneuvers even with large initial deviations.

Key words: reachable set, perching maneuver, iterative learning control, nonlinear systems, Zonotope

CLC Number:

V249

Huimin WU, Zhen HE, Yuxiao PENG. Iterative learning trajectory control method based on reachable ets for perching maneuvers[J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(10): 331227.

Figures/Tables 21

Fig.1

Table 1

Geometric parameters of fixed-wing UAV［28］

参数	数值
无人机质量 $m$ /mg	$0.8$
气动弦长 $c$ /m	$0.25$
翼展 $b$ /m	$1.0$
俯仰转动惯量 $I y$ /（kg·m²）	$0.1$
机翼面积 $S w i n g$	$0.25$
升降舵面积 $S e$ /m²	$0.054$
升降舵空气动力中心到无人机质心的距离 $l e$ /m	$0.235$
飞行器重心位置 $x c g$ /m	$0.2$
机翼的1/4弦长位置 $x c / 4 w i n g$ /m	$0.25$
升降舵的1/4弦长位置 $x c / 4 t a i l$ /m	$0.72$
重力加速度 $g$ /（m·s^-2）	$9.8$
空气密度 $ρ$ /（kg·m^-3）	$1.225$

Table 1

Fig.2

Table 2

Safety constraints during perching process

状态变量	下限值	上限值
$V / (m · s - 1)$	$0$	$25$
$α / r a d$	$- π / 2$	$π / 2$
$μ / r a d$	$- π / 4$	$π / 4$
$q / (r a d · s - 1)$	$- 3.5$	$3.5$
$x / m$	$0$	$15$
$h / m$	$0$	$10$
$δ e / r a d$	$- π / 3$	$π / 3$

Table 2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Table 3

Target area 𝒯 for landing maneuvers

状态变量	目标区域中心点值	目标允许偏差
$V / (m · s - 1)$	$4.014 6$	$Δ V < 0.500 0$
$μ / r a d$	$- 0.049 2$	$Δ μ < 0.300 0$
$α / r a d$	$0.855 4$	$Δ α < 0.300 0$
$q / (r a d · s - 1)$	$- 0.702 4$	$Δ q < 0.400 0$
$x / m$	$13.997 8$	$Δ x < 0.150 0$
$h / m$	$2.091 0$	$Δ h < 0.150 0$

Table 3

Fig.8

Fig.9

Fig.10

Fig.11

Fig.12

Fig.13

Fig.14

Fig.15

Fig.16

Fig.17

Table 4

Comparison of terminal control errors

控制方法	$Δ x$ /m	$Δ h$ /m
MPC	$0.442 3$	$0.122 2$
ILQR	$0.162 0$	$0.226 8$
基于可达集的迭代控制	$0.033 3$	$0.104 1$

Table 4

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Iterative learning trajectory control method based on reachable ets for perching maneuvers

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