Distributed Continuous-Time Optimization With Scalable Adaptive Event-Based Mechanisms

被引：33
|
作者：
Wu, Zizhen [1 ]
Li, Zhenhong [2 ]
Ding, Zhengtao [2 ]
Li, Zhongkui [1 ]
机构：
[1] Peking Univ, Dept Mech & Engn Sci, Coll Engn, State Key Lab Turbulence & Complex Syst, Beijing 100871, Peoples R China
[2] Univ Manchester, Sch Elect & Elect Engn, Manchester M13 9PL, Lancs, England
来源：
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS | 2020年 / 50卷 / 09期
基金：
英国科学技术设施理事会; 中国国家自然科学基金;
关键词：
Cost function; Heuristic algorithms; Laplace equations; Network topology; Adaptive systems; Adaptive control; cooperative control; distributed optimization; event-triggered control; CONVEX-OPTIMIZATION; COORDINATION; ALGORITHMS; CONSENSUS; NETWORKS;
D O I：
10.1109/TSMC.2018.2867175
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper investigates the distributed continuous-time optimization problem, which consists of a group of agents with variant local cost functions. An adaptive consensus-based algorithm with event triggering communications is introduced, which can drive the participating agents to minimize the global cost function and exclude the Zeno behavior. Compared to the existing results, the proposed event-based algorithm is independent of the parameters of the cost functions, using only the relative information of neighboring agents, and hence is fully distributed. Furthermore, the constraints of the convexity of the cost functions are relaxed.
引用
收藏
页码：3252 / 3257
页数：6
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