Adaptive control of nonlinear fractional-order systems using T–S fuzzy method

被引:0
|
作者
Saeed Mirzajani
Mohammad Pourmahmood Aghababa
Aghileh Heydari
机构
[1] Payame Noor University,Department of Mathematics
[2] Urmia University of Technology,Faculty of Electrical Engineering
关键词
T–S fuzzy control; Fractional-order systems; Adaptive approach; Lyapunov’s theory; Intelligent control;
D O I
暂无
中图分类号
学科分类号
摘要
Owing to the superior capability of fractional differential equations in modeling and characterizing accurate dynamical properties of many high technology real world systems, the design and control of fractional-order systems have captured lots of attention in recent decades. In this paper, an adaptive intelligent fuzzy approach to controlling and stabilization of nonlinear non-autonomous fractional-order systems is proposed. Since dynamic equations of applied fractional-order systems usually contain various parameters and nonlinear terms, the Takagi–Sugeno (T–S) fuzzy models with if-then rules are adopted to describe the system dynamics. Also, as the nonlinear system parameters are assumed to be unknown, adaptive laws are derived to estimate such fluctuations. Simple adaptive linear-like control rules are developed based on the T–S fuzzy control theory. The stability of the resulting closed loop system is guaranteed by Lyapunov’s stability theory. Two illustrative numerical examples are presented to emphasize the correct performance and applicability of the proposed adaptive fuzzy control methodology. It is worth to notice that the proposed controller works well for stabilization of a wide class of either autonomous nonlinear uncertain fractional-order systems or non-autonomous complex systems with unknown parameters.
引用
收藏
页码:527 / 540
页数:13
相关论文
共 50 条
  • [1] Adaptive control of nonlinear fractional-order systems using T-S fuzzy method
    Mirzajani, Saeed
    Aghababa, Mohammad Pourmahmood
    Heydari, Aghileh
    [J]. INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 2019, 10 (03) : 527 - 540
  • [2] Adaptive Fuzzy Backstepping Control of Fractional-Order Nonlinear Systems
    Liu, Heng
    Pan, Yongping
    Li, Shenggang
    Chen, Ye
    [J]. IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2017, 47 (08): : 2209 - 2217
  • [3] Adaptive fuzzy control for a class of nonlinear fractional-order systems
    Sun, Yeguo
    Li, Ling
    [J]. PROCEEDINGS OF THE 2019 31ST CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2019), 2019, : 3477 - 3482
  • [4] Command filtered adaptive fuzzy control of fractional-order nonlinear systems
    Ha, Shumin
    Chen, Liangyun
    Liu, Heng
    Zhang, Shaoyu
    [J]. EUROPEAN JOURNAL OF CONTROL, 2022, 63 : 48 - 60
  • [5] Consensus Control of Nonlinear Fractional-Order Multiagent Systems with Input Saturation: A T-S Fuzzy Method
    Hao, Yilin
    Fang, Zhiming
    Cao, Jinde
    Liu, Heng
    [J]. IEEE Transactions on Fuzzy Systems, 2024, 32 (12) : 6754 - 6766
  • [6] Fuzzy Adaptive Fault-Tolerant Control of Fractional-Order Nonlinear Systems
    Li, Yuan-Xin
    Wang, Quan-Yu
    Tong, Shaocheng
    [J]. IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2021, 51 (03): : 1372 - 1379
  • [7] Embedded adaptive fractional-order sliding mode control based on TSK fuzzy system for nonlinear fractional-order systems
    Esraa Mostafa
    Osama Elshazly
    Mohammad El-Bardini
    Ahmad M. El-Nagar
    [J]. Soft Computing, 2023, 27 : 15463 - 15477
  • [8] Embedded adaptive fractional-order sliding mode control based on TSK fuzzy system for nonlinear fractional-order systems
    Mostafa, Esraa
    Elshazly, Osama
    El-Bardini, Mohammad
    El-Nagar, Ahmad M. M.
    [J]. SOFT COMPUTING, 2023, 27 (21) : 15463 - 15477
  • [9] Adaptive Fuzzy Variable Structure Control of Fractional-Order Nonlinear Systems with Input Nonlinearities
    Shumin Ha
    Liangyun Chen
    Heng Liu
    [J]. International Journal of Fuzzy Systems, 2021, 23 : 2309 - 2323
  • [10] Adaptive Fuzzy Variable Structure Control of Fractional-Order Nonlinear Systems with Input Nonlinearities
    Ha, Shumin
    Chen, Liangyun
    Liu, Heng
    [J]. INTERNATIONAL JOURNAL OF FUZZY SYSTEMS, 2021, 23 (07) : 2309 - 2323