无人机集群分布式跟踪抗扰控制设计与实验验证

doi:10.13229/j.cnki.jdxbgxb.20221143

摘要/Abstract

摘要：

研究了存在外界未知扰动下分布式无人机集群轨迹跟踪问题，设计了一种新的无人机集群非线性鲁棒轨迹跟踪策略。首先，基于切向坐标系构造了无人机编队模型，并设计了协同控制律；其次，针对轨迹跟踪问题设计了一种基于误差符号函数积分（RISE）的鲁棒控制算法，用于补偿未知外界扰动的影响，提高了无人机编队系统的鲁棒性；然后，基于Lyapunov分析的方法，证明了无人机编队系统协同误差全局渐近收敛，以及位置跟踪误差半全局渐近收敛；最后，在四旋翼无人机编队实验平台上进行了无风扰和有风扰条件下无人机集群轨迹跟踪实验，并与常规滑模控制算法进行了性能对比实验。实验结果表明，所设计的控制律可以实现多无人机的协同轨迹跟踪，且具有较强的抗外界干扰能力。

关键词: 控制科学与工程, 无人机集群, 分布式跟踪控制, 协同控制, 抗扰控制, 实验验证

Abstract:

The distributed trajectory tracking problem for multiple UAVs under unknown external disturbance is investigated. A new nonlinear robust trajectory tracking strategy for multiple UAVs is proposed. The dynamic model for the UAVs' formation is illustrated in the Tangent frame. For the trajectory tracking problem， a robust formation tracking control strategy based on robust integral of the signum of error （RISE） is designed to compensate for the effects of unknown external disturbances， which improves the robustness of the UAVs' formation system. Based on the Lyapunov stability analysis， it is proved that the global asymptotic convergence of the coordination errors and the semi-global asymptotic convergence of the UAVs' position tracking errors are achieved. The proposed formation control strategy is validated via real-time experiments that are performed on the self-build UAVs' formation flight control testbed. Flights without wind disturbance and flights with disturbance are all performed. The performance comparison experiment with the conventional sliding mode control algorithm is performed. The experimental results show that the designed control strategy can achieve good coordinated trajectory tracking of multiple UAVs， and has better control performance compared with normal sliding mode control law.

Key words: control science and engineering, UAV formation, distributed trajectory tracking control, cooperative control, disturbance rejection control, experimental verification

中图分类号:

TP273

鲜斌,王印鑫,王岭. 无人机集群分布式跟踪抗扰控制设计与实验验证[J]. 吉林大学学报(工学版), 2024, 54(7): 2093-2103.

Bin XIAN,Yin-xin WANG,Ling WANG. Distributed robust tracking control for multiple unmanned aerial vehicles： theory and experimental verification[J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(7): 2093-2103.

图/表 14

图1

图2

图3

表1

飞行实验相关参数"

参数名称	参数值
$r d$	$[0,0, 1] T$
$Λ$	$d i a g ([5,10,10] T)$
$D v$	$d i a g ([9,9, 9] T)$
$k s$	$d i a g ([3,2, 1.5] T)$
$λ$	$d i a g ([2,1.6,2] T)$
$α$	$d i a g ([0.08,0.12,0.05] T)$
$β$	$d i a g ([0.006,0.008,0.006] T)$

表1

图4

图5

图6

图7

图8

图9

表2

实验1各无人机轨迹跟踪位置误差分析"

误差通道	SMC最大误差	RISEC最大误差	SMC均方根误差	RISEC均方根误差
$e 1 x / m$	0.161 1	0.024 0	0.060 2	0.008 7
$e 1 y / m$	0.287 6	0.024 2	0.114 4	0.008 3
$e 1 z / m$	0.045 7	0.021 7	0.016 2	0.008 2
$e 2 x / m$	0.270 6	0.061 4	0.096 6	0.024 7
$e 2 y / m$	0.239 4	0.039 5	0.082 1	0.015 5
$e 2 z / m$	0.058 2	0.030 6	0.017 0	0.014 4
$e 3 x / m$	0.246 4	0.073 0	0.108 8	0.021 5
$e 3 y / m$	0.212 9	0.083 4	0.088 1	0.024 7
$e 3 z / m$	0.044 3	0.042 9	0.015 4	0.014 8

表2

图10

表3

实验2各无人机轨迹跟踪位置误差分析"

误差通道	SMC 最大误差	RISEC 最大误差	SMC 均方根误差	RISEC 均方根误差
$e 1 x / m$	0.244 1	0.057 3	0.090 8	0.019 1
$e 1 y / m$	0.354 3	0.077 5	0.165 0	0.024 0
$e 1 z / m$	0.067 9	0.042 9	0.014 3	0.018 8
$e 2 x / m$	0.209 6	0.066 7	0.105 3	0.028 0
$e 2 y / m$	0.229 0	0.053 6	0.098 2	0.019 6
$e 2 z / m$	0.036 7	0.032 9	0.012 3	0.014 4
$e 3 x / m$	0.303 2	0.069 5	0.116 1	0.021 8
$e 3 y / m$	0.200 9	0.076 8	0.104 5	0.031 4
$e 3 z / m$	0.031 2	0.063 6	0.011 7	0.020 4

表3

图11

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