有向图上不确定性参数系统的有限时间辨识与自适应一致性控制

岳冬冬; 史建涛; 王钢; 曹进德

doi:10.16383/j.aas.c240382

有向图上不确定性参数系统的有限时间辨识与自适应一致性控制

doi: 10.16383/j.aas.c240382 cstr: 32138.14.j.aas.c240382

岳冬冬^{1, 2,},
史建涛^1,,
王钢^3,,
曹进德^4,

1.
南京工业大学电气工程与控制科学学院南京 211816
2.
东南大学复杂工程系统测量与控制教育部重点实验室南京 210096
3.
北京理工大学自主智能无人系统全国重点实验室北京 100081
4.
东南大学数学学院南京 211189

基金项目: 国家重点研发计划 (2021YFB1714800), 国家自然科学基金 (62373184, 62333010), 江苏省自然科学基金 (BK20240531), 江苏省高等学校自然科学基金 (24KJB120006), 中央高校基本科研业务费专项资金 (2242024k30037, 2242024k30038)资助

详细信息

作者简介:
岳冬冬：南京工业大学电气工程与控制科学学院副教授. 2021年获得东南大学控制科学与工程专业博士学位. 东南大学博士后. 主要研究方向为网络系统的分布式自适应辨识, 控制与优化. E-mail: yued@njtech.edu.cn

史建涛：南京工业大学电气工程与控制科学学院教授. 2016年获得清华大学控制科学与工程专业博士学位. 主要研究方向为最优与学习控制, 分布式协同控制, 故障检测与容错控制. 本文通信作者. E-mail: shjt@njtech.edu.cn

王钢：北京理工大学自主智能无人系统全国重点实验室教授. 2018年获得北京理工大学控制科学与工程专业博士学位, 同年获得明尼苏达大学电气与计算机工程专业博士学位. 明尼苏达大学博士后. 主要研究方向为信号处理, 强化学习, 信息物理系统, 多智能体系统. E-mail: gangwang@bit.edu.cn

曹进德：东南大学首席教授. 1998年获得四川大学应用数学专业博士学位. 香港中文大学博士后. IEEE会士, 俄罗斯科学院院士, 欧洲科学院院士. 主要研究方向为复杂网络与复杂系统, 神经动力学与优化. E-mail: jdcao@seu.edu.cn

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出版历程
- 收稿日期: 2024-07-08
- 录用日期: 2024-10-27
- 网络出版日期: 2025-01-07

Finite-time Identification and Adaptive Consensus Control of Uncertain Parametric Systems Over Directed Graphs

YUE Dong-Dong^{1, 2
,},
SHI Jian-Tao^1
,,
WANG Gang^3
,,
CAO Jin-De^4
,

1.
College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 211816
2.
Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Southeast University, Nanjing 210096
3.
State Key Laboratory of Autonomous Intelligent Unmanned Systems, Beijing Institute of Technology, Beijing 100081
4.
School of Mathematics, Southeast University, Nanjing 211189

Funds: Supported by National Key Research and Development Program of China (2021YFB1714800), National Natural Science Foundation of China (62373184, 62333010), Natural Science Foundation of Jiangsu Province (BK20240531), Natural Science Foundation of the Higher Education Institutions of Jiangsu Province (24KJB120006), and the Fundamental Research Funds for the Central Universities (2242024k30037, 2242024k30038)

More Information

Author Bio:
YUE Dong-Dong　Associate professor at the College of Electrical Engineering and Control Science, Nanjing Tech University. He received his Ph.D. degree in control science and engineering from Southeast University in 2021. He was a postdoctor at Southeast University. His research interest covers distributed adaptive identification, control, and optimization of network systems

SHI Jian-Tao　Professor at the College of Electrical Engineering and Control Science, Nanjing Tech University. He received his Ph.D. degree in control science and engineering from Tsinghua University in 2016. His research interest covers optimal and learning control, distributed and cooperative control, and fault diagnosis and fault-tolerant control. Corresponding author of this paper

WANG Gang　Professor at the State Key Laboratory of Autonomous Intelligent Unmanned Systems, Beijing Institute of Technology. He received his Ph.D. degree in control science and engineering from Beijing Institute of Technology, and his Ph.D. degree in electrical and computer engineering from the University of Minnesota, both in 2018. He was a postdoctor at University of Minnesota. His research interest covers signal processing, reinforcement learning, cyber-physical systems, and mult-iagent systems

CAO Jin-De　Endowed chair professor of Southeast University. He received his Ph.D. degree in applied mathematics from Sichuan University in 1998. He was a postdoctor at Chinese University of Hong Kong. He is elected as a fellow of IEEE, a member of Russian Academy of Sciences, a member of the Academy of Europe. His research interest covers complex networks and complex systems, and neural dynamics and optimization

摘要

摘要: 针对有向图上一类不确定性多智能体系统, 研究一体化参数辨识与一致性控制策略. 在现有模型参考自适应一致性(Model reference adaptive consensus, MRACon)框架基础上, 结合动态回归因子扩张与融合(Dynamic regressor extension and mixing, DREM)技术, 提出两类改进的MRACon控制协议, 即固定耦合DREM-MRACon和自适应耦合DREM-MRACon. 其中, 自适应耦合DREM-MRACon在有向支撑树结构上具有时变的耦合增益. 与MRACon相比, 此两类改进算法均能保证系统未知参数的有限时间辨识. 此外, 固定耦合DREM-MRACon保证了多智能体系统的指数时间一致性, 而自适应耦合DREM-MRACon则克服了对于全局网络拓扑特征信息的依赖性. 最后, 通过数值仿真验证了理论成果的有效性.
- 多智能体系统 /
- 自适应控制 /
- 参数辨识 /
- 有向图 /
- 自适应一致性
Abstract: The paper addresses an integrated parameter identification and consensus control problem of a class of uncertain multi-agent systems over directed graphs. Based on the existing model reference adaptive consensus (MRACon) framework and by leveraging dynamic regressor extension and mixing (DREM) techniques, two improved MRACon protocols are established, namely fixed-coupling DREM-MRACon and adaptive-coupling DREM-MRACon. The latter protocol contains time-varying coupling gains along a directed spanning tree. As compared to the baseline MRACon, both of the proposed protocols ensure finite-time identification of the system＇s unknown parameters. Additionally, fixed-coupling DREM-MRACon achieves exponential consensus of the agents, while adaptive-coupling DREM-MRACon overcomes the requirement of global information on the network topology. Finally, numerical simulations are conducted to verify the effectiveness of the theoretical results.
- Multi-agent systems /
- adaptive control /
- parameter identification /
- directed graphs /
- adaptive consensus

HTML全文

图 1 多智能体系统的通讯图$\mathcal{G}_1$(红色部分为一个支撑树$\bar{\mathcal{G}}_1$)

Fig. 1 The communication graph $\mathcal{G}_1 $of the multi-agent systems (with a spanning tree $\bar{\mathcal{G}}_1$ highlighted in red)

下载: 全尺寸图片幻灯片

图 2 基于文献[30]提出的MRACon (即式(4))求解得到的一致性误差和参数辨识误差

Fig. 2 The consensus errors and parameter identification errors by MRACon (4) proposed in [30]

下载: 全尺寸图片幻灯片

图 3 基于本文提出的固定耦合DREM-MRACon求解得到的一致性误差和参数辨识误差

Fig. 3 The consensus errors and parameter identification errors by the proposed fixed-coupling DREM-MRACon

下载: 全尺寸图片幻灯片

图 4 多智能体系统的状态轨迹与参数辨识轨迹

Fig. 4 The state trajectories and parameter identification trajectories of the multi-agent systems

下载: 全尺寸图片幻灯片

图 5 基于本文提出的自适应耦合DREM-MRACon求解得到的一致性误差和参数辨识误差

Fig. 5 The consensus errors and parameter identification errors by the proposed adaptive-coupling DREM-MRACon

下载: 全尺寸图片幻灯片

图 6 自适应耦合增益的演化曲线

Fig. 6 The evolution of the adaptive coupling gains

下载: 全尺寸图片幻灯片

图 7 无人艇的偏转状态(偏转角和偏转角速度)轨迹

Fig. 7 The evolution of the yaw states (yaw angle and yaw rate) of the USVs

下载: 全尺寸图片幻灯片

图 8 分别基于本文提出的DREM-MRACon与文献[30]中的基础MRACon得到的误差曲线

Fig. 8 The error curves by the proposed DREM-MRACon and by the baseline MRACon^[30]

下载: 全尺寸图片幻灯片

表 1 参数辨识的精确时刻$ T_i $

Table 1 The exact time $T_i$ for parameter identification

智能体序号 MRACon^[30] 固定耦合
DREM-MRACon 自适应耦合
DREM-MRACon

1 — 6.84 7.43

2 — 2.77 2.75

3 — 2.16 2.26

4 — 2.03 1.74

5 — 1.74 1.78

6 — 1.55 1.74

下载: 导出CSV

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