基于改进一致性算法的无人机编队控制

doi:10.7527/S1000-6893.2020.23848

电子电气工程与控制

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基于改进一致性算法的无人机编队控制

吴宇¹, 梁天骄²

1. 重庆大学航空航天学院, 重庆 400044;
2. 歼击机综合仿真航空科技重点实验室, 成都 610091

收稿日期:2020-01-17 修回日期:2020-02-11 出版日期:2020-09-15 发布日期:2020-05-21
通讯作者: 吴宇 E-mail:cquwuyu@cqu.edu.cn
基金资助:
中央高校基本科研业务费（2019CDJGFHK001）

Improved consensus-based algorithm for unmanned aerial vehicle formation control

WU Yu¹, LIANG Tianjiao²

1. College of Aerospace Engineering, Chongqing University, Chongqing 400044, China;
2. Key Laboratory of Aviation Science and Technology on Fighter Integrated Simulation, Chengdu 610091, China

Received:2020-01-17 Revised:2020-02-11 Online:2020-09-15 Published:2020-05-21
Supported by:
The fundamental Research Funds for the Central Universities (2019CDJGFHK001)

摘要/Abstract

摘要： 编队飞行是指多架无人机保持以一定队形进行飞行的状态，相比于单架飞机执行任务，无人机编队能够增加搜索面积，提高飞机飞行性能，增大完成任务成功率。编队控制是实现编队安全高效完成指定任务的前提。本文以一致性理论为基础，针对无人机运动模型的特点与实际飞行要求，对基本的一致性算法进行改进，提出了改进一致性无人机编队控制算法。首先利用纵向和横侧向解耦的自动驾驶仪模型给出了无人机的三自由度运动方程，根据机动性与飞行性能要求定义了各方向上的加速度、速度与角速度约束。基于一致性理论，将编队控制分为平面与纵向2个方向进行，在状态控制的基础上，利用各状态变量间的几何关系对无人机运动自由度进行转换，加入编队队形信息，设计了编队控制算法。为了使算法生成的指令信号满足约束条件，提出了"最小调整"约束条件处理策略。依据粒子群算法对各无人机的爬升加速度进行优化，以避免机间碰撞。仿真结果表明：提出的编队控制算法具备编队成形与变换功能，能够使无人机编队状态快速收敛到指定值，且保持指定队形，无人机飞行状态满足所有约束条件。

关键词: 无人机编队, 编队控制, 一致性理论, 约束条件处理, 粒子群算法

Abstract: Formation flight refers to the state in which multiple UAVs keep flying in a specific configuration. Compared to single UAVs, UAV formation can increase the search area, enhance flight performance and raise the success rate of missions. UAV formation control is the premise for a safe and efficient mission. In this paper, the standard consensus algorithm is improved according to the characteristics of the UAV motion model and the demand of actual flight, and an improved consensus-based formation control algorithm is proposed. First, the 3-DOF kinetic equations of UAVs are established based on the decoupled autopilot model, and the constraints on acceleration, velocity and angular rates are defined considering the maneuverability and flight performance of UAVs. The formation control is also carried out in the horizonal plane and vertical direction respectively. Based on the state control, the DOFs of UAVs are transformed using the geometrical relationship between different states of UAVs, and the formation control algorithm is designed integrating the configuration information. Furthermore, a constraint handling strategy named 'minimum adjustment’ is developed to enable the command signals to meet all the constraints. The collision between UAVs is avoided by optimizing the climbing acceleration of UAVs with the Particle Swarm Optimization (PSO) algorithm. Simulation results demonstrate the ability of the proposed formation control algorithm to form or change the formation. The states and configuration of the UAV formation can quickly converge to the specific values, and the states of UAVs satisfy all the constraints.

Key words: UAV formation, formation control, consensus theories, constraint handling, particle swarm optimization

中图分类号:

V211.3
TP273

吴宇, 梁天骄. 基于改进一致性算法的无人机编队控制[J]. 航空学报, 2020, 41(9): 323848-323848.

WU Yu, LIANG Tianjiao. Improved consensus-based algorithm for unmanned aerial vehicle formation control[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2020, 41(9): 323848-323848.

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主管单位：中国科学技术协会主办单位：中国航空学会北京航空航天大学

基于改进一致性算法的无人机编队控制

Improved consensus-based algorithm for unmanned aerial vehicle formation control

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