多无人机仿射编队的抗扰控制设计
Robust control design of affine formation control for multiple unmanned aerial vehicles
鲜斌 1刘佳妮 1姜鹏志2
作者信息
- 1. 天津大学电气自动化与信息工程学院,天津 300072
- 2. 天津大学电气自动化与信息工程学院,天津 300072;航天神舟飞行器有限公司,天津 300301
- 折叠
摘要
多无人机仿射编队控制能够根据编队需求实现灵活的编队机动.针对存在外界未知扰动情况下的多无人机编队形成及变换问题,研究多无人机仿射编队抗扰控制及编队形成与变换过程中的避碰问题.首先,针对在外界干扰情况下仿射编队形成及变换问题,设计一种基于误差符号函数积分(RISE)的鲁棒控制算法.该控制算法能够对未知外界扰动造成的影响进行有效的补偿,提高多无人机仿射编队控制的鲁棒性.然后,基于Lyapunov分析法证明了编队控制系统的稳定性.针对多无人机机间避碰问题,利用缓冲维诺单元对无人机飞行区域进行分离,在仿射编队形成及变换过程中保证无人机间的碰撞避免.针对避碰过程中可能出现的死锁问题,提出一种启发式算法使无人机能够顺利从死锁中恢复.最后,使用室内实验平台验证所提出算法的有效性.
Abstract
Multi-UAV affine formation control enables flexible formation maneuvering according to formation needs.This paper proposes a robust affine formation control strategy for a group of UAVs which is subjected to unknown external disturbances.Collision avoidance is also achieved with the proposed control strategy.A robust control algorithm based on the robust integral of the signum of error(RISE)is developed to deal with issues associated with the formation and transformation problem of affine formation under external disturbances.This control algorithm can effectively compensate for the effects of unknown external disturbances and enhance the robustness of the multi-UAV affine formation.The stability of the formation control system is proved via Lyapunov based stability analysis.To address the issue of collision avoidance among multiple UAVs,the space occupied by each UAV is partitioned using a buffered voronoi cell(BVC)method.Then the collision avoidance during both the affine formation and transformation can be achieved.To deal with the possible deadlock problem that may arise during the collision avoidance process,a heuristic algorithm is designed to facilitate the smooth recovery of UAVs from deadlock.Finally,the proposed algorithm's effectiveness is validated through the experimental platform.
关键词
多无人机/仿射编队控制/分布式控制/抗扰控制/机间避碰/实验验证Key words
multiple UAVs/affine formation control/distributed control/disturbance rejection control/collision avoidance/experimental verification引用本文复制引用
出版年
2024
NETL
NSTL
万方数据