Distributed Fixed-time Optimization Control for Multi-agent Systems With Set Constraints

被引:0
作者
Chen G. [1 ]
Li Z.-Y. [1 ]
机构
[1] College of Automation, Chongqing University, Chongqing
来源
Zidonghua Xuebao/Acta Automatica Sinica | 2022年 / 48卷 / 09期
基金
中国国家自然科学基金;
关键词
Distributed optimization; fixed-time convergence; gradient method; multiagent systems; set constraints;
D O I
10.16383/j.aas.c190416
中图分类号
O24 [计算数学];
学科分类号
070102 ;
摘要
This paper studies a class of optimization problem with local constraints for multi-agent systems, and a distributed optimization algorithm with fixed-time convergence is proposed. The proposed distributed controller consists of local projection module, consensus module and gradient module. The local projection module is used to ensure that the states converge to the local constraint set in the fixed time; the consensus module with time-varying gain guarantees the fixed-time state consensus; the gradient module with time-varying gain ensures the states converge to the optimal solution in fixed-time. The fixed-time convergence of the proposed algorithm is analyzed by using the convex optimization and the fixed-time Lyapunov theory. Since the upper bound of convergence time for the proposed algorithm does not depend on the initial condition, it possible to predesign the convergence time according to the task requirements. Finally, numerical simulations verify the effectiveness of the theoretical results. © 2022 Science Press. All rights reserved.
引用
收藏
页码:2254 / 2264
页数:10
相关论文
共 35 条
[11]  
Qiu Z R, Liu S, Xie L H., Distributed constrained optimal consensus of multi-agent systems, Automatica, 68, pp. 209-215, (2016)
[12]  
Yi P, Hong Y G, Liu F., Distributed gradient algorithm for constrained optimization with application to load sharing in power systems, Systems and Control Letters, 83, pp. 42-52, (2015)
[13]  
Le X Y, Chen S J, Yan Z, Xi J T., A neurodynamic approach to distributed optimization with globally coupled constraints, IEEE Transactions on Cybernetics, 48, 11, pp. 3149-3158, (2018)
[14]  
Yang S F, Liu Q S, Wang J., A multi-agent system with a proportional-integral protocol for distributed constrained optimization, IEEE Transactions on Automatic Control, 62, 7, pp. 3461-3467, (2017)
[15]  
Rahili S, Ren W., Distributed continuous-time convex optimization with time-varying cost functions, IEEE Transactions on Automatic Control, 62, 4, pp. 1590-1605, (2017)
[16]  
Zhang Qing, Gong Zhi-Kun, Yang Zheng-Quan, Chen Zeng-Qiang, Distributed optimization for adaptive flocking of multiagent systems, Control Theory and Applications, 36, 4, pp. 666-672, (2019)
[17]  
Song Y F, Chen W S., Finite-time convergent distributed consensus optimisation over networks, IET Control Theory and Applications, 10, 11, pp. 1314-1318, (2016)
[18]  
Hu Z L, Yang J Y., Distributed finite-time optimization for second order continuous-time multiple agents systems with time-varying cost function, Neurocomputing, 287, 11, (2018)
[19]  
Santilli M, Marino A, Gasparri A., A finite-time protocol for distributed continuous-time optimization of sum of locally coupled strictly convex functions, Proceedings of the 2018 IEEE Conference on Decision and Control, pp. 993-998, (2018)
[20]  
Lin P, Ren W, Farrell J A., Distributed continuous-time optimization: Nonuniform gradient gains, finite-time convergence, and convex constraint set, IEEE Transactions on Automatic Control, 62, 5, pp. 2238-2253, (2017)