A fixed-time gradient algorithm for distributed optimization with inequality constraints

被引：6
作者：
He, Xing [1 ]
Wei, Boyu [1 ]
Wang, Hui [2 ]
机构：
[1] Southwest Univ, Coll Elect & Informat Engn, Chongqing Key Lab Nonlinear Circuits & Intelligent, Chongqing 400715, Peoples R China
[2] Chongqing Normal Univ, Sch Math Sci, Chongqing 401331, Peoples R China
来源：
NEUROCOMPUTING | 2023年 / 532卷
关键词：
Gradient algorithm; Fixed time convergence; Distributed optimization; Load sharing control; MULTIAGENT SYSTEMS; CONVERGENCE ANALYSIS; CONSENSUS;
D O I：
10.1016/j.neucom.2023.02.022
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, a distributed fixed time gradient algorithm for neurodynamic systems is proposed for solv-ing optimization problems with local inequality constraints. The algorithm is designed using fixed time theory and sliding model control techniques, where each agent has a local objective function known only to itself, and the optimal solution of each local objective function sum can be obtained in a fixed time by the information interaction between neighbors under the condition of local inequality constraints. In addition, the upper bound of the fixed time can be obtained and it is proved theoretically that the upper bound of the fixed time is independent of the initial value. Finally, the stability and effectiveness of the algorithm are verified by numerical examples.(c) 2023 Elsevier B.V. All rights reserved.
引用
收藏
页码：106 / 113
页数：8
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