无人飞行器集群自主控制: 预设性能驱动的安全编队控制

方浩; 赵欣悦; 陈杰

doi:10.16383/j.aas.c240603

无人飞行器集群自主控制: 预设性能驱动的安全编队控制

doi: 10.16383/j.aas.c240603 cstr: 32138.14.j.aas.c240603

方浩^1,,
赵欣悦^1,,
陈杰^{1, 2,}

1.
北京理工大学自动化学院北京 100081
2.
自主智能无人系统全国重点实验室/同济大学上海 201210

基金项目: 国家重点研发计划(2022YFA1004703, 2022YFB4702000), 国家自然科学基金(62373048, 62133002, U1913602, 62088101), 上海市重大科技专项(2021SHZDZX0100)资助

详细信息

作者简介:
方浩：北京理工大学自动化学院教授. 1995年获西安理工大学学士学位, 1998年和2002年分别获西安交通大学硕士学位和博士学位. 主要研究方向为全地形移动机器人、机器人控制和多智能体系统. 本文通信作者. E-mail: fangh@bit.edu.cn

赵欣悦：北京理工大学自动化学院博士研究生. 2019年获得沈阳理工大学自动化专业学士学位. 主要研究方向为安全控制, 多智能体系统. E-mail: xinyue.zhao@bit.edu.cn

陈杰：中国工程院院士, 北京理工大学自动化学院教授, 同济大学教授, 自主智能无人系统全国重点实验室教授. 1986年、1996年和2001年分别获得北京理工大学控制理论与应用专业学士学位、硕士学位和博士学位. 主要研究方向为复杂系统、多智能体系统、多目标优化与决策和约束非线性控制. E-mail: chenjie@bit.edu.cn

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- 被引次数: 0
出版历程
- 收稿日期: 2024-08-30
- 录用日期: 2024-12-13
- 网络出版日期: 2025-02-12

Autonomous Control of Unmanned Aerial Vehicle Swarms: Prescribed Performance Driven Safety Formation Control

FANG Hao^1
,,
ZHAO Xin-Yue^1
,,
CHEN Jie^{1, 2
,}

1.
School of Automation, Beijing Institute of Technology, Beijing 100081
2.
National Key Laboratory of Autonomous Intelligent Unmanned Systems/Tongji University, Shanghai 201210

Funds: Supported by National Key Research and Development Program of China (2022YFA1004703, 2022YFB4702000), National Natural Science Foundation of China (62373048, 62133002, U1913602, 62088101), and Shanghai Major Science and Technology Special Project (2021SHZDZX0100)

More Information

Author Bio:
FANG Hao　Professor at School of Automation, Beijing Institute of Technology. He received his B.S. degree from Xi'an University of Technology in 1995, and the M.S. and Ph.D. degrees from Xi'an Jiaotong University in 1998 and 2002, respectively. His research interests include all-terrain mobile robots, robotic control, and multi-agent systems. Corresponding author of this paper

ZHAO Xin-Yue　Ph.D. candidate at School of Automation, Beijing Institute of Technology. He received his B.S. degree in automation from Shenyang Ligong University in 2019. His research interest covers safety control and multi-agent system

CHEN Jie　Academician of Chinese Academy of Engineering, professor at Tongji University, School of Automation, Beijing Institute of Technology, and National Key Laboratory of Autonomous Intelligent Unmanned Systems. He received his B.Sc., M.Sc., and Ph.D. degrees in control science and application from Beijing Institute of Technology in 1986, 1996, and 2001, respectively. His research interests include complex systems, multi-agent system, multi-objective optimization and decision, and constrained nonlinear control

摘要

摘要: 针对障碍环境下多无人机编队跟踪问题, 提出了一种兼顾编队跟踪性能与安全的控制框架. 在该框架中, 首先利用性能边界可调的预设性能控制 (Prescribed performance control, PPC) 方法生成期望控制信号, 使无人机跟踪虚拟领导者的期望轨迹, 跟踪过程中满足瞬态与稳态误差约束. 进一步, 基于控制障碍函数 (Control barrier function, CBF) 描述无人机的安全状态集合并建立二次规划问题, 利用 Karush-Kuhn-Tucker (KKT) 条件得到最小干预安全控制器的闭式解. 最后, 利用安全控制的闭式解构造辅助系统, 实现性能函数的自适应更新. 理论分析表明, 该算法能够在编队跟踪与安全性冲突条件下确保系统安全, 在不发生冲突时实现性能约束下的编队跟踪. 仿真结果验证了提出算法的有效性.
- 多无人机编队跟踪 /
- 安全控制 /
- 预设性能控制 /
- 控制障碍函数 /
- 辅助系统
Abstract: A control framework that balances formation tracking performance and safety is proposed to address the problem of multiple Unmanned Aerial Vehicles (UAVs) formation tracking within obstacle environments. In this framework, a prescribed performance control (PPC) method with adjustable performance boundaries is first used to generate the desired control signals, ensuring that the UAVs track the desired trajectory generated by the virtual leader while satisfying transient and steady-state error constraints during the tracking process. Furthermore, the control barrier function (CBF) is used to describe the safety state set of the UAVs, and a quadratic programming problem is constructed. The closed-form solution of the minimum intervention safety controller is obtained using the Karush-Kuhn-Tucker (KKT) conditions. Finally, an auxiliary system is constructed based on the closed-form solution of the safe control to achieve adaptive updating of the performance function. Theoretical analysis shows that this algorithm can ensure system safety under conditions of conflict between formation tracking and safety, while achieving formation tracking under performance constraints when no conflict occurs. Simulation results validate the effectiveness of the proposed algorithm.
- Multi-UAV formation tracking /
- safety control /
- prescribed performance control (PPC) /
- control barrier function (CBF) /
- auxiliary system
注释:

1) 1^1[29]针对系统(4), 关于其状态的连续可微标量函数$ b({\boldsymbol{x}}): {\bf{R}}^{\bf{n}}\to{\bf{R}} $的相对度指的是沿着系统动力学求导直至出现$ {\boldsymbol{u}} $时的求导次数

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图 1 队形定义与编队跟踪示意图

Fig. 1 Formation definition and formation tracking diagram

下载: 全尺寸图片幻灯片

图 2 预设性能安全控制框架

Fig. 2 Prescribed performance safety control framework

下载: 全尺寸图片幻灯片

图 3 编队跟踪轨迹

Fig. 3 Formation tracking trajectory

下载: 全尺寸图片幻灯片

图 4 编队跟踪误差曲线

Fig. 4 Trajectory of formation tracking error

下载: 全尺寸图片幻灯片

图 5 与障碍物之间距离的变化

Fig. 5 Evolution of distance between UAV and obstacles

下载: 全尺寸图片幻灯片

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