Bandwidth scheduling and optimization using non-cooperative game model-based shuffled frog leaping algorithm in a networked learning control system

被引:0
|
作者
Lijun Xu
Minrui Fei
Tinggang Jia
T. C. Yang
机构
[1] Shanghai University,Shanghai Key Laboratory of Power Station Automation Technology
[2] Shanghai Electric Group Co.,Central Academe
[3] University of Sussex,undefined
来源
关键词
Networked control system; Bandwidth scheduling; Non-cooperative game; Shuffled frog leaping algorithm; Data rate; Fairness;
D O I
暂无
中图分类号
学科分类号
摘要
In this paper, under a general framework of Networked two-layer Learning Control Systems (NLCSs), optimal and fair network scheduling is studied. Multi-networked feedback control loops called subsystems in an NLCS share a common communication media. Therefore, there is a competition for the available bandwidth. A non-cooperative game fairness model is first formulated, and then the utility function of subsystems is designed. This takes into account of a number of factors, namely transmission data rate, sampling, the feature of scheduling pattern adopted, and networked control. For the problem defined above, the existence and uniqueness of Nash equilibrium point are proved. Following this, an evolutionary algorithm appeared in the literature recently, shuffled frog leaping algorithm, is improved applying to obtain an optimal solution. The algorithm has a high convergence rate, and the comparison simulation results have demonstrated the effectiveness of the proposed theoretical approach and the algorithm applied.
引用
收藏
页码:1117 / 1128
页数:11
相关论文
共 50 条
  • [1] Bandwidth scheduling and optimization using non-cooperative game model-based shuffled frog leaping algorithm in a networked learning control system
    Xu, Lijun
    Fei, Minrui
    Jia, Tinggang
    Yang, T. C.
    NEURAL COMPUTING & APPLICATIONS, 2012, 21 (06): : 1117 - 1128
  • [2] Non-cooperative Game Model Based Bandwidth Scheduling and the Optimization of Quantum-Inspired Weight Adaptive PSO in a Networked Learning Control System
    Xu, Lijun
    Fei, Minrui
    Yang, T. C.
    LIFE SYSTEM MODELING AND INTELLIGENT COMPUTING, PT II, 2010, 98 : 8 - +
  • [3] Iterative Learning Control Based on Niche Shuffled Frog Leaping Algorithm Research
    Hao, Xiaohong
    Wang, Dongjiang
    PROCEEDINGS OF THE 2017 2ND INTERNATIONAL CONFERENCE ON AUTOMATION, MECHANICAL CONTROL AND COMPUTATIONAL ENGINEERING (AMCCE 2017), 2017, 118 : 166 - 172
  • [4] Shuffled Frog Leaping Algorithm Research Based Optimal Iterative Learning Control
    Hao Xiaohong
    Wang Hua
    Li Zhuoyue
    Gu qun
    PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON ADVANCES IN MECHANICAL ENGINEERING AND INDUSTRIAL INFORMATICS, 2015, 15 : 854 - 860
  • [5] Non-cooperative network scheduling game model of streaming media based surveillance system and its optimization based on genetic algorithm
    Jiang, Yibo
    Wang, Wanliang
    Jin, Jing
    1800, Science Press, Beijing, 100085, China (28):
  • [6] Non-cooperative job scheduling game model and its implementation in networked manufacturing
    Xi'an Jiaotong University, Xi'an 710049, China
    Zhongguo Jixie Gongcheng, 2006, 8 (819-822):
  • [7] Scheduling algorithm in distributed systems based on non-cooperative game
    Tong, Zhao
    Xiao, Zheng
    Li, Ken-Li
    Liu, Hong
    Li, Jun
    Hunan Daxue Xuebao/Journal of Hunan University Natural Sciences, 2016, 43 (10): : 139 - 147
  • [8] Networked Optimization for Demand Side Management based on Non-cooperative Game
    Li, Chaojie
    Yu, Xinghuo
    Yu, Wenwu
    Huang, Tingwen
    PROCEEDINGS 2015 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL INFORMATICS (INDIN), 2015, : 1159 - 1164
  • [9] Power control algorithm in cognitive radio system based on modified Shuffled Frog Leaping Algorithm
    Zhang, Xiaodan
    Zhang, Yifeng
    Shi, Yuhui
    Zhao, Li
    Zou, Cairong
    AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS, 2012, 66 (06) : 448 - 454
  • [10] Shuffled frog-leaping algorithm using elite opposition-based learning
    Zhao, Jia
    Lv, Li
    INTERNATIONAL JOURNAL OF SENSOR NETWORKS, 2014, 16 (04) : 244 - 251