Adaptive Fuzzy Variable Structure Control of Fractional-Order Nonlinear Systems with Input Nonlinearities

被引:11
|
作者
Ha, Shumin [1 ]
Chen, Liangyun [1 ]
Liu, Heng [2 ]
机构
[1] Northeast Normal Univ, Sch Math & Stat, Changchun 130024, Peoples R China
[2] Guangxi Univ Nationalities, Coll Math & Phys, Nanning 530006, Peoples R China
基金
中国国家自然科学基金;
关键词
Riemann-Liouville fractional-order nonlinear system; Caputo fractional-order nonlinear system; Adaptive fuzzy control; Dead-zone; Input nonlinearity; TRACKING CONTROL; CHAOTIC SYSTEMS; MODEL; SYNCHRONIZATION; STABILITY; OBSERVER;
D O I
10.1007/s40815-021-01105-x
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The unknown dead-zone input nonlinearities (DZINs) are considered in the Riemann-Liouville fractional-order nonlinear systems (FONSs) and the Caputo FONSs in this paper. The unknown DZINs in the FONSs will cause FONSs instability. In this paper, by using the fractional-order Lyapunov stability theory, a variable structure adaptive fuzzy control (AFC) scheme is designed to solve the unknown DZINs in the FONSs. The unknown terms of the FONSs and the uncertain terms of DZINs are handled by fuzzy logic systems (FLSs). The parameters boundedness of FLSs is guaranteed via the constructed fractional-order adaptive laws (FOALs). By using FLSs, this paper does not need to know the exact values of gain reduction tolerances (GRTs) in the unknown DZINs, which makes the constructed scheme more suitable for the actual system. The scheme proposed in this paper can be used to effectively control the Riemann-Liouville FONSs and the Caputo FONSs with/without unknown DZINs. Finally, three simulation results verify the AFCs we designed are effective for both Riemann-Liouville FONSs and Caputo FONSs with unknown DZINs.
引用
收藏
页码:2309 / 2323
页数:15
相关论文
共 50 条
  • [41] Adaptive composite dynamic surface neural control for nonlinear fractional-order systems subject to delayed input
    Liu, Siwen
    Wang, Huanqing
    Li, Tieshan
    ISA TRANSACTIONS, 2023, 134 : 122 - 133
  • [42] Adaptive Fuzzy Control for Fractional-Order Interconnected Systems With Unknown Control Directions
    Liang, Bingyun
    Zheng, Shiqi
    Ahn, Choon Ki
    Liu, Feng
    IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2022, 30 (01) : 75 - 87
  • [43] Synchronization of fractional-order chaotic systems based on adaptive fuzzy control
    Chen Ye
    Li Sheng-Gang
    Liu Heng
    ACTA PHYSICA SINICA, 2016, 65 (17)
  • [44] Adaptive soft variable structure controller with control constraints for synchronization of fractional-order chaotic systems
    Shao K.-Y.
    Guo H.-X.
    Han F.
    Zhang Y.
    Wang J.-C.
    Kongzhi yu Juece/Control and Decision, 2019, 34 (06): : 1325 - 1330
  • [45] Neuro-Fuzzy-Based Adaptive Dynamic Surface Control for Fractional-Order Nonlinear Strict-Feedback Systems With Input Constraint
    Song, Shuai
    Zhang, Baoyong
    Song, Xiaona
    Zhang, Zhengqiang
    IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2021, 51 (06): : 3575 - 3586
  • [46] Adaptive iterative learning control for a class of fractional-order nonlinear systems
    Hao, Xiuqing
    Liu, Xiaoli
    SECOND INTERNATIONAL CONFERENCE ON PHYSICS, MATHEMATICS AND STATISTICS, 2019, 1324
  • [47] Historical Data-Driven Composite Learning Adaptive Fuzzy Control of Fractional-Order Nonlinear Systems
    Qiu, Hongling
    Liu, Heng
    Zhang, Xiulan
    INTERNATIONAL JOURNAL OF FUZZY SYSTEMS, 2023, 25 (03) : 1156 - 1170
  • [48] Adaptive Fuzzy Decentralized Control for Fractional-Order Nonlinear Large-Scale Systems With Unmodeled Dynamics
    Sui, Shuai
    Zhan, Yongliang
    Jin, Junwei
    Chen, C. L. Philip
    Tong, Shaocheng
    IEEE ACCESS, 2021, 9 : 142594 - 142604
  • [49] Event-Triggered Fuzzy Adaptive Control of Incommensurate Fractional-Order Nonlinear Systems with Prescribed Performance
    Gong D.
    Wang Y.
    IEEE Transactions on Circuits and Systems II: Express Briefs, 2023, 70 (12) : 4489 - 4493
  • [50] Adaptive Fuzzy Decentralized Dynamic Surface Control for Fractional-Order Nonlinear Large-Scale Systems
    Zhan, Yongliang
    Li, Yongming
    Tong, Shaocheng
    IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2022, 30 (08) : 3373 - 3383