Analysis of grey wolf optimizer based fractional order PID controller in speed control of DC motor

被引:53
作者
Agarwal, Jeetendra [1 ]
Parmar, Girish [1 ]
Gupta, Rajeev [1 ]
Sikander, Afzal [2 ]
机构
[1] Rajasthan Tech Univ, Dept Elect Engn, Kota 324010, India
[2] Dr BR Ambedkar Natl Inst Technol, Dept Instrumentat & Control Engn, Jalandhar 144011, Panjab, India
来源
MICROSYSTEM TECHNOLOGIES-MICRO-AND NANOSYSTEMS-INFORMATION STORAGE AND PROCESSING SYSTEMS | 2018年 / 24卷 / 12期
关键词
OPTIMUM DESIGN;
D O I
10.1007/s00542-018-3920-4
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The present work deals with comparative and robustness analysis of grey wolf optimization (GWO) based fractional order proportional-integral derivative (FOPID) controller for speed control of DC motor. The GWO is a meta-heuristic algorithm inspired from the social hunting behaviour of grey wolves as search agents. The GWO algorithm maintains a proper balance between exploration and exploitation processes. The integral of time multiplied absolute error (ITAE) has been taken as an objective function for obtaining the parameters of FOPID controller by GWO. Comparison of proposed GWO/ FOPID approach with other existing techniques has also been shown along with GWO/PID. It has been observed that proposed approach with ITAE as an objective function gives less settling, rise times and comparable overshoot in comparison to existing approaches in the literature. The robustness analysis of GWO/FOPID approach has also been carried out with variations in the parameters of DC motor.
引用
收藏
页码:4997 / 5006
页数:10
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] Fractional Order Controllers versus Integer Order Controllers
    Dulau, Mircea
    Gligor, Adrian
    Dulau, Tudor-Mircea
    [J]. 10TH INTERNATIONAL CONFERENCE INTERDISCIPLINARITY IN ENGINEERING, INTER-ENG 2016, 2017, 181 : 538 - 545
  • [3] A particle swarm optimization approach for optimum design of PID controller in AVR system
    Gaing, ZL
    [J]. IEEE TRANSACTIONS ON ENERGY CONVERSION, 2004, 19 (02) : 384 - 391
  • [4] Khalilpuor M, 2011, MAJ C EL ENG MAJ TOW
  • [5] Khanam I., 2017, 4 IEEE UTT PRAD SECT
  • [6] Madadi A., 2014, TECH J ENG APPL SCI, V4, P373
  • [7] Type-2 fuzzy neural network using grey wolf optimizer learning algorithm for nonlinear system identification
    Mao, Wei-Lung
    Suprapto
    Hung, Chung-Wen
    [J]. MICROSYSTEM TECHNOLOGIES-MICRO-AND NANOSYSTEMS-INFORMATION STORAGE AND PROCESSING SYSTEMS, 2018, 24 (10): : 4075 - 4088
  • [8] How effective is the Grey Wolf optimizer in training multi-layer perceptrons
    Mirjalili, Seyedali
    [J]. APPLIED INTELLIGENCE, 2015, 43 (01) : 150 - 161
  • [9] Grey Wolf Optimizer
    Mirjalili, Seyedali
    Mirjalili, Seyed Mohammad
    Lewis, Andrew
    [J]. ADVANCES IN ENGINEERING SOFTWARE, 2014, 69 : 46 - 61
  • [10] Nasri M, 2007, PROC WRLD ACAD SCI E, V20, P211