On PI-controller parameters adjustment for rolling mill drive current loop using neural tuner

被引:0
作者
Eremenko, Y. [1 ]
Glushchenko, A. [1 ]
Petrov, V. [1 ]
机构
[1] Stary Oskol Technol Inst, Stary Oskol, Russia
来源
XII INTERNATIONAL SYMPOSIUM INTELLIGENT SYSTEMS 2016, (INTELS 2016) | 2017年 / 103卷
基金
俄罗斯基础研究基金会;
关键词
current control loop; direct current drive; neural tuner; PI-controller; rolling mill;
D O I
10.1016/j.procs.2017.121
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We consider a problem of an on-line tuning of linear controller parameters used for a rolling mill drive control. A neural tuner of PI-controller for current control loop is developed. It allows to adjust Kp and Ki according to armature winding parameters values change. Moreover, if initial values of controller parameters do not provide required transients quality, proposed system will tune them to solve this problem. A functional block diagram, a neural network structure, and a rule base are shown. Two sets of modelling experiments are conducted with a roll mill model. Obtained results show transients quality improvement in speed and current control loops. In addition to this, energy consumption of the control system with the tuner is lower by 1-2% comparing to conventional PI-controller basic system. (C)2017 The Authors. Published by Elsevier B.V.
引用
收藏
页码:355 / 362
页数:8
相关论文
共 14 条
  • [1] PID control system analysis, design, and technology
    Ang, KH
    Chong, G
    Li, Y
    [J]. IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2005, 13 (04) : 559 - 576
  • [2] [Anonymous], 2008, AL RAFIDAIN ENG
  • [3] [Anonymous], 1952, T AM SOC MECH ENG
  • [4] [Anonymous], 1995, NEUROCONTROL ITS APP
  • [5] Astrom K J., 2006, ISA - The Instrumentation, Systems and Automation Society
  • [6] On estimating the efficiency of a neural optimizer for the parameters of a PID controller for heating objects control
    Eremenko, Yu I.
    Poleshchenko, D. A.
    Glushchenko, A. I.
    Litvinenko, A. M.
    Ryndin, A. A.
    Podval'nyi, E. S.
    [J]. AUTOMATION AND REMOTE CONTROL, 2014, 75 (06) : 1137 - 1144
  • [7] Hjalmarsson H., 1999, Proceedings of the 14th World Congress. International Federation of Automatic Control, P445
  • [8] MULTILAYER FEEDFORWARD NETWORKS ARE UNIVERSAL APPROXIMATORS
    HORNIK, K
    STINCHCOMBE, M
    WHITE, H
    [J]. NEURAL NETWORKS, 1989, 2 (05) : 359 - 366
  • [9] Jing Zhang, 2012, Proceedings of the 2012 International Conference on Measurement, Information and Control (MIC), P791, DOI 10.1109/MIC.2012.6273408
  • [10] ShuMei Zhang, 2011, 2011 International Conference on Electrical and Control Engineering (ICECE), P1950