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摘要:
针对无人机运动学模型特点和远距离成形问题，提出一种基于改进一致性算法的无人机集结-成形策略。建立能直观描述编队队形的坐标系，根据纵向和横航向解耦的带自动驾驶仪的无人机三自由度运动学模型的特点，考虑无人机机动性能约束，对一致性算法进行改进，实现对无人机速度、航向和飞行高度的控制，提出编队队形控制算法。针对无人机初始间距大带来的调参问题，增加集结过程，并利用粒子群算法优化集结速度，避免航迹冲突，集结结束后再采用所提算法生成无人机航迹，提升算法的适应性。仿真结果表明：所提算法能使无人机在满足机动性约束的情况下，形成稳定队形；相比于直接成形法，所提策略提高改进一致性算法的适应性和安全性。
Abstract:According to the characteristics of unmanned aerial vehicle kinematics model and the problem of remote forming, an improved consensus-based algorithm was proposed to solve the gathering-forming strategy of unmanned aerial vehicle. The coordinate system which can describe the formation directly was established. According to the characteristics of the three degree-of-freedom kinematics model of unmanned aerial vehicle with autopilot decoupled from longitudinal and transverse directions and the constraints of unmanned aerial vehicle maneuvering performance, the consensus algorithm was improved to realize the control of unmanned aerial vehicle speed, heading and flight altitude. The formation control algorithm was proposed. In addressing the parameter tuning problem caused by the large initial spacing of unmanned aerial vehicle, a gathering process was added. The particle swarm optimization algorithm was used to optimize the gathering speed to avoid trajectory conflicts, and the proposed algorithm was used to generate trajectory of each unmanned aerial vehicle after the gathering, both of which improved the adaptability of the algorithm. The simulation results show that the proposed algorithm can make unmanned aerial vehicles form a stable formation under the condition of satisfying the maneuverability constraints. Compared with the direct forming method, the proposed strategy improves the adaptability and security of the improved consistency algorithm.
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图 1 Oxgyg坐标系与Oxryr坐标系的关系
Figure 1. Position relation between Oxgyg coordinate system and Oxryr coordinate system
图 2 编队成形飞行航迹及状态响应
Figure 2. Trajectories and state response of formation forming flight
图 5 无人机初始间距较大时的编队成形
Figure 5. Formation forming when the initial distance between UAVs is large
图 7 含无人机集结的编队成形飞行航迹
Figure 7. Trajectories of formation forming with UAVs gathering phase
表 1 无人机机动性能参数
Table 1. Maneuvering performance parameters of UAV
vmax/
(m·s−1)$ \begin{array}{c} {v_{\min }}/\\ ({\mathrm{m}} \cdot {{\mathrm{s}}^{ - 1}}) \end{array}$ $ \begin{array}{c} {a_{\max }}/\\ ({\mathrm{m}} \cdot {{\mathrm{s}}^{ - 2}}) \end{array} $ $ \begin{array}{c} {a_{\min }}/\\({\mathrm{m}} \cdot {{\mathrm{s}}^{ - 2}}) \end{array}$ $ \begin{array}{c}{\omega _{\max }}/ \\ ({\mathrm{rad}} \cdot {{\mathrm{s}}^{ - 1}}) \end{array} $ $ \begin{array}{c} {\omega _{\min }}/ \\ ({\mathrm{rad}} \cdot {{\mathrm{s}}^{ - 1}})\end{array} $ $ \begin{array}{c}{\alpha _{\max }}/ \\({\mathrm{rad}} \cdot {{\mathrm{s}}^{ - 2}})\end{array} $ $ \begin{array}{c}{\alpha _{\min }}/ \\({\mathrm{rad}} \cdot {{\mathrm{s}}^{ - 2}}) \end{array}$ $\begin{array}{c} v_{\max }^{\textit{z}}/ \\({\mathrm{m}} \cdot {{\mathrm{s}}^{ - 1}}) \end{array}$ $\begin{array}{c} v_{\min }^{\textit{z}}/ \\({\mathrm{m}} \cdot {{\mathrm{s}}^{ - 1}})\end{array} $ $ \begin{array}{c}a_{\max }^{\textit{z}}/ \\({\mathrm{m}} \cdot {{\mathrm{s}}^{ - 2}}) \end{array}$ $ \begin{array}{c}a_{\min }^{\textit{z}}/ \\({\mathrm{m}} \cdot {{\mathrm{s}}^{ - 2}})\end{array} $ 300 50 49 −49 $ \dfrac{{\text{π}}}{9} $ $ - \dfrac{{\text{π}}}{9} $ $ \dfrac{{\text{π}}}{{12}} $ $ - \dfrac{{\text{π}}}{{12}} $ 20 −20 4 −4 
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表 2 无人机初始位置信息
Table 2. Initial position information of UAVs
无人机 $ x/{{\mathrm{m}}} $ $ y/{{\mathrm{m}}} $ $ {\textit{z}}/{{\mathrm{m}}} $ UAV1 −3 565 −2 846 0 UAV2 4 098 3 415 0 UAV3 1 372 2 588 0 UAV4 −3 360 1 612 0
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