LQ Synchronization of Discrete-Time Multiagent Systems: A Distributed Optimization Approach

被引：54

作者：

Wang, Qishao ^{[1
]}

Duan, Zhisheng ^{[1
]}

Wang, Jingyao ^{[2
,3
]}

Chen, Guanrong ^{[4
]}

机构：

[1] Peking Univ, State Key Lab Turbulence & Complex Syst, Dept Mech & Engn Sci, Coll Engn, Beijing 100871, Peoples R China

[2] Xiamen Univ, Dept Automat, Xiamen 361000, Fujian, Peoples R China

[3] Xiamen Univ, Shenzhen Res Inst, Shenzhen 518000, Guangdong, Peoples R China

[4] City Univ Hong Kong, Dept Elect Engn, Hong Kong, Peoples R China

来源：

IEEE TRANSACTIONS ON AUTOMATIC CONTROL | 2019年 / 64卷 / 12期

基金：

中国国家自然科学基金; 国家重点研发计划;

关键词：

Synchronization; Optimization; Multi-agent systems; Heuristic algorithms; Topology; Transient response; Indexes; Control system; distributed optimization; heterogeneous system; synchronization; CONSENSUS; FEEDBACK;

D O I：

10.1109/TAC.2019.2910950

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The linear quadratic (LQ) synchronization problem for multiagent systems is solved by developing a distributed algorithm. It is the first time in the literature to formulate the LQ synchronization problem consisting of auxiliary synchronization state variables in discrete time with a finite horizon. The solution to this LQ synchronization problem is first considered in a centralized setting by leveraging connections to an alternating direction method of multiplier and then extended to a distributed setting, in which the individual agents control inputs can be computed independently, thereby making the solution scalable. Numerical examples for both homogeneous and heterogeneous multiagent systems are given to demonstrate the effectiveness of the proposed method.

引用

页码：5183 / 5190

页数：8

共 30 条

[1]

Bellman R. E., 1972, Dynamic Programming

[2] Distributed optimization and statistical learning via the alternating direction method of multipliers [J].

Boyd S. ;

Parikh N. ;

Chu E. ;

Peleato B. ;

Eckstein J. .

Foundations and Trends in Machine Learning, 2010, 3 (01) :1-122

[3]

Boyd Stephen P., 2014, Convex Optimization

[4] Optimal Linear-Consensus Algorithms: An LQR Perspective [J].

Cao, Yongcan ;

Ren, Wei .

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 2010, 40 (03) :819-830

[5] Distributed Finite-Time Cooperative Control of Multiple High-Order Nonholonomic Mobile Robots [J].

Du, Haibo ;

Wen, Guanghui ;

Cheng, Yingying ;

He, Yigang ;

Jia, Ruting .

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2017, 28 (12) :2998-3006

[6]

Facchinei F, 2003, SPRING S OPERAT RES, V1

[7] First-Order Algorithms for Convex Optimization with Nonseparable Objective and Coupled Constraints [J].

Gao X. ;

Zhang S.-Z. .

Zhang, Shu-Zhong (zhangs@umn.edu), 1600, Springer Science and Business Media Deutschland GmbH (05) :131-159

[8] Distributed L2-gain output-feedback control of homogeneous and heterogeneous systems [J].

Jiao, Qiang ;

Modares, Hamidreza ;

Lewis, Frank L. ;

Xu, Shengyuan ;

Xie, Lihua .

AUTOMATICA, 2016, 71 :361-368

[9] LQ Control for Coordinated Linear Systems [J].

Kempker, Pia L. ;

Ran, Andre C. M. ;

van Schuppen, Jan H. .

IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2014, 59 (04) :851-862

[10] Robust Consensus of Linear Feedback Protocols Over Uncertain Network Graphs [J].

Li, Zhongkui ;

Chen, Jie .

IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2017, 62 (08) :4251-4258

← 1 2 3 →