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,NM PC)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.
A method of UAV motion control to optimize air-ground relay network
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.
万方数据
维普
