面向空地中继网络优化的无人机运动控制方法

doi:10.12305/j.issn.1001-506X.2024.05.24

摘要/Abstract

摘要：

本文提出一种基于模型的通信中继无人机运动控制方法, 旨在提高地面车辆编队的网络连通性和通信性能。通过联合考虑未知多用户移动性、环境对信道特性的影响以及接收信号的不可用到达角信息来解决中继无人机运动控制问题。该方法主要由两部分构成: ①利用图论中的最小生成树构建网络连接性并定义通信性能指标, 该网络连接性同时考虑了地面节点与无人机的通信链路及地面节点与地面节点的通信链路; ②针对移动节点的通信中继, 提出一种改进粒子群优化(particle swarm optimization, PSO)和非线性模型预测控制(nonlinear model predictive control, NMPC)相结合的中继无人机运动控制策略, 其中移动节点的未来位置由卡尔曼滤波器进行预测。在单一环境和复杂环境下的仿真结果表明, 所提出的运动控制方法可以驱使无人机到达或跟踪最优中继位置的运动并提高网络性能, 同时论证了考虑环境对信道的影响是有益的。

关键词: 无人机, 中继通信, 运动控制, 最小生成树, 非线性模型预测控制, 改进粒子群优化

Abstract:

In this paper, a model-based motion control method for communication relay unmanned aerial vehicle is proposed to improve the network connectivity and communication performance of ground vehicle formation. The problem of relay unmanned aerial vehicle motion control is solved by considering the unknown multi-user mobility, the impact of the environment on the channel characteristics and the unavailable arrival angle information of the received signal. The method is mainly composed of two parts: (ⅰ) The minimum spanning tree in graph theory is used to construct network connectivity and define communication performance indicators. The network connectivity takes into account the communication links between ground nodes and unmanned aerial vehicles and between ground nodes and ground nodes; (ⅱ) For the communication relay of mobile nodes, a relay unmanned aerial vehicle motion control strategy combining improved particle swarm optimization (PSO) and nonlinear model predictive control (NMPC) is proposed, in which the future position of mobile nodes is predicted by a Kalman filter. The simulation results in a single and complex environment show that the proposed motion control method can drive the unmanned aerial vehicle to reach or track the optimal relay position and improve the network performance. At the same time, it is beneficial to consider the influence of environment on the channel.

Key words: unmanned aerial vehicle, relay communication, motion control, minimum spanning tree, nonlinear model predictive control (NMPC), improved particle swarm optimization (PSO)

中图分类号:

TP301

陶灿灿, 周锐. 面向空地中继网络优化的无人机运动控制方法[J]. 系统工程与电子技术, 2024, 46(5): 1712-1723.

Cancan TAO, Rui ZHOU. A method of UAV motion control to optimize air-ground relay network[J]. Systems Engineering and Electronics, 2024, 46(5): 1712-1723.

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信道参数	城市郊区	城区	高密度城区	高楼区
α	0.1	0.3	0.5	0.5
β	750	500	300	300
γ	8	5	20	50
a	5.018 8	9.610 1	11.948 0	27.156 2
b	0.351 1	0.159 2	0.135 9	0.122 8
η_LoS/dB	0.1	1.0	1.6	2.3
η_NLoS/dB	21	22	23	34

环境区域	覆盖中心/m		覆盖半径/m
环境区域	X	Y	覆盖半径/m
城市郊区	5 000	5 000	10 000
城区	6 500	4 500	3 500
高密度城区	7 000	3 500	2 000
高楼区	7 700	4 000	600

参数	数值
无人机速度v₀/(m/s)	40
无人机飞行高度h/m	400
转弯率约束(ω_min, ω_max)/(rad/s)	(-0.3, 0.3)
最小转弯半径r_min/m	133.3
地面无人车速度v_g/(m/s)	5
路径衰减因子λ_g	5
放缩因子α	10
基准信号强度S′_t/dBm	-110
权重因子p_c	1e1
权重因子q_c	2
权重因子r_ω	1e5
改进PSO的学习因子c₁	3
改进PSO的学习因子c₂	3
采样周期T_s/s	0.5
滚动优化步数N_r	5

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