基于分布式模型预测控制的无人机编队控制
作者:
作者单位:

1.海军航空大学;2.92635部队

作者简介:

通讯作者:

中图分类号:

V249

基金项目:

中国博士后科学基金,国家自然科学基金项目(面上项目,重点项目,重大项目)


Formation Control of Multi-UAV Based on Distributed Model Predictive Control Algorithm
Author:
Affiliation:

Naval Aviation University

Fund Project:

China Postdoctoral Science Foundation,The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)

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    摘要:

    针对多四旋翼无人机编队在巡航飞行过程中队形形成与保持问题,采用分布式模型预测控制方法,将该问题转化为在线滚动优化问题。首先建立线性时不变的编队运动模型,进而在考虑状态和输入约束、不考虑时延、外界干扰、噪声的情况下,利用领航跟随策略设计了一种分布式模型预测控制器,通过引入自身和邻居的假设状态轨迹来设计代价函数。其中,邻居信息的交互是在有向、时不变通信拓扑结构下进行的。基于该控制器,无人机能够在跟踪目标轨迹的同时,快速形成预先设定的队形并保持队形飞行。通过引入终端等式约束以保证系统稳定,进而将目标函数作为Lyapunov函数,给出了编队系统渐进稳定的充分条件。最后,利用6架无人机仿真验证了控制算法的有效性、优越性。

    Abstract:

    This paper presents a distributed model predictive control algorithm for the formation and maintenance of multi-quadrotor during the cruise fight. That is dealing with the formation control by the rolling optimization method. Firstly, a linear time-invariant formation motion model is established. And then using the leader-follower strategy, a distributed model predictive controller is designed by introducing the assumed state trajectory of itself and neighbors to the cost function, which is in the case of considering the state and input constraints, without considering the communication delay, external interference, and noise. Unmanned aerial vehicles (UAVs) interact with local information based on a directional, time-invariant communication topology. Based on the controller, UAVs can quickly form a pre-set formation and maintain it while tracking the target trajectory. To ensure the stability of the system, the terminal equality constraint is introduced. And then taking the cost function as the Lyapunov function, the sufficient conditions for the asymptotic stability of the formation system are given. Finally, Simulations with six UAVs demonstrate the effectiveness and superiority of the proposed algorithm.

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历史
  • 收稿日期:2021-03-17
  • 最后修改日期:2021-06-08
  • 录用日期:2021-06-17
  • 在线发布日期: 2021-07-01
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