Practical implementation for stable adaptive interval A2-C0 type-2 TSK fuzzy controller

被引:7
|
作者
El-Nagar, Ahmad M. [1 ]
机构
[1] Menoufia Univ, Dept Ind Elect & Control Engn, Fac Elect Engn, Menoufia 32852, Egypt
关键词
Interval type-2 fuzzy logic; TSK; Gradient method; Lyapunov theorem; Shunt wound DC machine; PID CONTROLLER; STABILITY ANALYSIS; SYSTEMS; DESIGN; LOGIC; OPTIMIZATION; REALIZATION; PD;
D O I
10.1007/s00500-018-3523-0
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper presents a systematic method to design an adaptive interval type-2 Takagi-Sugeno-Kang fuzzy logic controller (AIT2 TSK FLC) that is like a PI-type fuzzy controller to satisfy certain desired performance. The proposed AIT2 TSK FLC consists of two parts: the IT2 TSK FLC and the adaptation mechanism. The antecedents and consequents for the TSK FLC are IT2FSs and crisp numbers, respectively, which are named as A2-C0. In this paper, the gradient method can be used to adapt both the antecedent and consequent parameters for the TSK FLC to minimize some criterion function. The stability of the proposed AIT2 A2-C0 TSK FLC is derived using the Lyapunov theorem. The proposed AIT2 A2-C0 TSK FLC is designed and realized using a microcontroller for controlling the shunt wound DC machine. The results show that the ability of the proposed AIT2 A2-C0 TSK FLC to improve the system performance in terms of the set-point tracking, the external disturbances, and the parameter uncertainties.
引用
下载
收藏
页码:9585 / 9603
页数:19
相关论文
共 50 条
  • [41] Interval Type-2 Fuzzy Controller for Ball and Beam Model
    Chokli, Mohammed N.
    Hote, Yogesh V.
    2013 INTERNATIONAL CONFERENCE ON CONTROL COMMUNICATION AND COMPUTING (ICCC), 2013, : 28 - 32
  • [42] A unified general type-2 fuzzy PID controller and its comparative with type-1 and interval type-2 fuzzy PID controller
    Shi, Jianzhong
    ASIAN JOURNAL OF CONTROL, 2022, 24 (04) : 1808 - 1824
  • [43] T2FELA: Type-2 Fuzzy Extreme Learning Algorithm for Fast Training of Interval Type-2 TSK Fuzzy Logic System
    Deng, Zhaohong
    Choi, Kup-Sze
    Cao, Longbing
    Wang, Shitong
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2014, 25 (04) : 664 - 676
  • [44] Recommendations on designing practical interval type-2 fuzzy systems
    Wu, Dongrui
    Mendel, Jerry M.
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2019, 85 : 182 - 193
  • [45] A Synchronizing Controller Using a Direct Adaptive Interval Type-2 Fuzzy Sliding Mode Strategy
    Hosseini, S. A.
    Akbarzadeh-T, M. -R.
    Naghibi-Sistani, M. -B.
    2013 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ - IEEE 2013), 2013,
  • [46] Development of a Robust Interval Type-2 TSK Fuzzy Logic Controlled UAV Platform
    Abel Hailemichael
    Ali Karimoddini
    Journal of Intelligent & Robotic Systems, 2023, 107
  • [47] A variable selection method for a hierarchical interval type-2 TSK fuzzy inference system *
    Wei, Xiang-Ji
    Zhang, Da-Qing
    Huang, Sheng-Juan
    FUZZY SETS AND SYSTEMS, 2022, 438 : 46 - 61
  • [48] An Approach for Construction and Learning of Interval Type-2 TSK Neuro-Fuzzy Systems
    Ouyang, Chen-Sen
    Liu, Shiu-Ling
    2009 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-3, 2009, : 1517 - 1522
  • [49] Development of a Robust Interval Type-2 TSK Fuzzy Logic Controlled UAV Platform
    Hailemichael, Abel
    Karimoddini, Ali
    JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS, 2023, 107 (02)
  • [50] Interval type-2 TSK fuzzy logic systems using hybrid learning algorithm
    Méndez, GM
    Castillo, O
    FUZZ-IEEE 2005: PROCEEDINGS OF THE IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS: BIGGEST LITTLE CONFERENCE IN THE WORLD, 2005, : 230 - 235