基于比例积分调节的严格反馈多智能体系统最优一致性

武文强; 王庆领

doi:10.16383/j.aas.c240288

基于比例积分调节的严格反馈多智能体系统最优一致性

doi: 10.16383/j.aas.c240288

武文强^{1, 2,},
王庆领^{1, 2,}

1.
东南大学自动化学院南京 210096
2.
复杂工程系统测量与控制教育部重点实验室南京 210096

基金项目: 国家自然科学基金(62373102), 江苏省自然科学基金(BK20221455), 安徽省重点研究与开发计划(2022i01020013)资助

详细信息

作者简介:
武文强：东南大学自动化学院博士研究生. 主要研究方向为多智能体系统与分布式优化. E-mail: wqwu@seu.edu.cn

王庆领：东南大学自动化学院教授. 主要研究方向为多智能体系统自适应控制, 分布式协同控制. 本文通信作者. E-mail: csuwql@gmail.com

中图分类号: 10.16383/j.aas.c240288
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- 被引次数: 0
出版历程
- 收稿日期: 2024-05-28
- 录用日期: 2024-08-31
- 网络出版日期: 2024-09-23

Optimal Consensus for Strict Feedback Multi-agent Systems Based on Proportional-integral Regulation

WU Wen-Qiang^{1, 2
,},
WANG Qing-Ling^{1, 2
,}

1.
School of Automation, Southeast University, Nanjing 210096
2.
Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Nanjing 210096

Funds: Supported by National Natural Science Foundation of China (62373102), Jiangsu Natural Science Foundation (BK20221455), and Anhui Provincial Key Research and Development Project (2022i01020013)

More Information

Author Bio:
WU Wen-Qiang Ph.D. candidate at the School of Automation, Southeast University. His research interest covers multi-agent systems and distributed optimization

WANG Qing-Ling Professor at the School of Automation, Southeast University. His research interest covers adaptive control of multi-agent systems and distributed cooperative control. Corresponding author of this paper

摘要

摘要: 本文研究了严格反馈多智能体系统的最优一致性问题, 旨在局部信息交互的条件下, 使所有智能体收敛至全局代价函数的最优解. 首先, 针对权重非平衡有向图, 提出了一种新的分布式比例积分(Proportional-integral, PI)变量, 将最优一致性问题转化为PI调节问题, 使得经典的控制技术能够通过调节PI变量的方式来处理更加复杂的多智能体系统. 然后, 结合所提出的分布式PI变量和预设性能控制, 设计了一类基于PI调节的最优一致性算法, 用以解决带有死区输入非线性和有界扰动的严格反馈多智能体系统的最优一致性问题. 最后, 通过仿真实验验证了所设计的最优一致性算法的有效性.
- 比例积分调节 /
- 最优一致性 /
- 严格反馈系统 /
- 多智能体系统 /
- 预设性能控制
Abstract: In this paper, the optimal consensus problem for strict-feedback multi-agent systems is investigated. The goal is to ensure that all agents converge to the optimal solution of the global cost function using only local exchanged information. First, a new type of distributed Proportional-integral (PI) variables is proposed for weight-unbalanced directed graphs, transforming the optimal consensus problem into a PI regulation problem. This transformation allows classical control techniques to handle more complicated multi-agent systems by regulating the PI variables. Subsequently, combining the proposed distributed PI variables with prescribed performance control, we design a class of optimal consensus algorithms based on PI regulation. These algorithms are used to solve the optimal consensus problem for strict feedback multi-agent systems with dead-zone input nonlinearity and bounded disturbances. Finally, the effectiveness of the proposed optimal consensus algorithms is verified through a simulation experiment.
- Proportional-integral regulation /
- optimal consensus /
- strict feedback system /
- multi-agent systems /
- prescribed performance control

HTML全文

图 1 权重非平衡有向图

Fig. 1 The weight-unbalanced directed graph

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图 2 不同算法下状态变量$ x_{i1}(t) $的轨迹((a)文献[18]算法; (b)本文算法)

Fig. 2 The trajectories of the state $ x_{i1}(t)$ under different algorithms ((a) the algorithm in [18]; (b) the proposed algorithm)

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图 3 状态变量$x_{i2}(t)$的轨迹

Fig. 3 The trajectories of the state $x_{i2}(t)$

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图 4 状态变量$x_{i3}(t)$的轨迹

Fig. 4 The trajectories of state $x_{i3}(t)$

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图 5 全局代价函数梯度$\sum_{i = 1}^{5}\nabla f_i(x_{i1}(t))$的轨迹

Fig. 5 The trajectory of global cost function gradient $\sum_{i = 1}^{5}\nabla f_i(x_{i1}(t))$

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图 6 PI变量$q_{i}(t)$的轨迹

Fig. 6 The trajectories of the PI variables $q_{i}(t)$

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图 7 变量$\hat{r}_i(t)$的轨迹

Fig. 7 The trajectories of $\hat{r}_i(t)$

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