Using Stochastic Approximation Type Algorithm for Choice of Consensus Protocol Step-Size in Changing Conditions

作     者：Amelina, Natalia Granichin, Oleg Granichina, Olga Ivanskiy, Yury Jiang, Yuming 

作者机构：Saint Petersburg State University Faculty of Mathematics and Mechanics Research Laboratory for Analysis and Modeling of Social Processes 7-9 Universitetskaya Nab. St. Petersburg 199034 Russia  St. Petersburg Russia Herzen State Pedagogical University St. Petersburg Russia Department of Telematics Norwegian University of Science and Technology TrondheimNO-7491 Norway 

出 版 物：《IFAC-PapersOnLine》 

年 卷 期：2016年第49卷第13期

页      面：265-269页

核心收录：

基　　金：★★ This work was supported by Russian Foundation for Basic Research  projects ★ This work was supported by Russian Foundation for Basic Research  projects n★uThbiesrw1o5r-k0w8-a0s2s6u4p0poanrtded1b6y-0R1u-0s0si7a5n9FaonudndSaatiionnt PfoerteBrsabsiucrgReSsteaatercUh npivroejrescittys This work was supported by Russian Foundation for Basic Research  projects gramntbe6r.3175.1-0881-.20021644.0 and 16-01-00759 and Saint Petersburg State University number 15-08-02640 and 16-01-00759 and Saint Petersburg State University grant 6.37.181.2014. grant 6.37.181.2014 

主　　题：Stochastic systems Approximation algorithms Approximation theory Multi agent systems Resource allocation Stochastic control systems consensus achievement Consensus protocols Control protocols Multiagent networks Optimal step size Simulation example Stochastic approximations Voting protocols 

摘      要：In the paper a multi-agent network system of different computing nodes is considered. A problem of load balancing in the network is addressed. The problem is formulated as consensus achievement problem and solved via local voting protocol. Agents exchange information about their states in presence of noise in communication channels. At certain moment network system topology changes and new step size of control protocol is chosen to meet new conditions. Step size adjustment is done by stochastic approximation type algorithm. Analytically obtained optimal step size values are given. Simulation example demonstrating step size adjustment is provided. © 2016