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 neural output-feedback control for nonstrict-feedback time-delay fractional-order systems with output constraints and actuator nonlinearities
    Zouari, Farouk
    Ibeas, Asier
    Boulkroune, Abdesselem
    Cao, Jinde
    Arefi, Mohammad Mehdi
    NEURAL NETWORKS, 2018, 105 : 256 - 276
  • [42] Command Filter-Based Adaptive Fuzzy Finite-Time Tracking Control for Uncertain Fractional-Order Nonlinear Systems
    You, Xingxing
    Dian, Songyi
    Liu, Kai
    Guo, Bin
    Xiang, Guofei
    Zhu, Yuqi
    IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2023, 31 (01) : 226 - 240
  • [43] Observer-based command filtered adaptive fuzzy control for fractional-order MIMO nonlinear systems with unknown dead zones
    Zhang, Xiulan
    Zhang, Weiye
    Cao, Jinde
    Liu, Heng
    EXPERT SYSTEMS WITH APPLICATIONS, 2024, 255
  • [44] Fractional-order switching type control law design for adaptive sliding mode technique of 3D fractional-order nonlinear systems
    Yin, Chun
    Cheng, Yuhua
    Zhong, Shou-Ming
    Bai, Zhanbing
    COMPLEXITY, 2016, 21 (06) : 363 - 373
  • [45] Output Feedback Adaptive Fuzzy Control for Uncertain Fractional-Order Nonlinear Switched System with Output Quantization
    Tang, Xuening
    Zhai, Ding
    Fu, Zhumu
    Wang, Huimin
    INTERNATIONAL JOURNAL OF FUZZY SYSTEMS, 2020, 22 (03) : 943 - 955
  • [46] Adaptive Fuzzy Control for Nonlinear Fractional-Order Uncertain Systems with Unknown Uncertainties and External Disturbance
    Li, Ling
    Sun, Yeguo
    ENTROPY, 2015, 17 (08): : 5580 - 5592
  • [47] Adaptive Robust Tracking Control for Multiple Unknown Fractional-Order Nonlinear Systems
    Gong, Ping
    Lan, Weiyao
    IEEE TRANSACTIONS ON CYBERNETICS, 2019, 49 (04) : 1365 - 1376
  • [48] Adaptive backstepping fuzzy synchronization control of fractional-order chaotic systems with input saturation and external disturbances
    Lin, Ming
    Zhang, Xiulan
    Qiu, Huiming
    AIP ADVANCES, 2023, 13 (08)
  • [49] State-Feedback Control for Fractional-Order Nonlinear Systems Subject to Input Saturation
    Luo, Junhai
    MATHEMATICAL PROBLEMS IN ENGINEERING, 2014, 2014
  • [50] A New Adaptive Robust Sliding Mode Control Approach for Nonlinear Singular Fractional-Order Systems
    Chen, Shunan
    Huang, Wenkai
    Liu, Qiang
    FRACTAL AND FRACTIONAL, 2022, 6 (05)