Observer-Based Adaptive Fuzzy Quantized Control for Fractional-Order Nonlinear Time-Delay Systems with Unknown Control Gains

被引:3
作者
Dong, Yuwen [1 ]
Song, Shuai [2 ]
Song, Xiaona [2 ]
Tejado, Ines [3 ]
机构
[1] Henan Univ Sci & Technol, Int Educ Coll, Luoyang 467023, Peoples R China
[2] Henan Univ Sci & Technol, Sch Informat Engn, Luoyang 467023, Peoples R China
[3] Univ Extremadura, Escuela Ingn Ind, Badajoz 06006, Spain
基金
中国国家自然科学基金;
关键词
adaptive quantized control; dynamic surface control; fractional-order nonlinear time-delay systems; fuzzy logic systems; Nussbaum gain technique; OUTPUT-FEEDBACK CONTROL; NEURAL-CONTROL;
D O I
10.3390/math12020314
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
This paper investigates the observer-based adaptive fuzzy quantized control problem for a class of fractional-order nonlinear time-delay systems with unknown control gains based on a modified fractional-order dynamic surface control (FODSC) technique and an indirect Lyapunov method. First, a fractional-order, high-gain state observer is constructed to estimate unavailable state information. Furthermore, the Nussbaum gain technique and a fractional-order filter are adopted to cope with the problem of unknown control gains and to reduce the computational complexity of the conventional recursive procedure, respectively. Moreover, through integration with the compensation mechanism and estimation model, the adaptive fuzzy quantized controllers and adaptive laws are designed to ensure that all the signals of the closed-loop system are bounded. In the end, the proposed controller is applied to a numerical example and a single-machine-infinite bus (SMIB) power system; the simulation results show the validity, superiority, and application potential of the developed control strategy.
引用
收藏
页数:24
相关论文
共 45 条
[1]  
[Anonymous], 1999, Fractional differential equations
[2]   Observer-based adaptive fuzzy controller for nonlinear systems with unknown control directions and input saturation [J].
Askari, Mohammad Reza ;
Shahrokhi, Mohammad ;
Talkhoncheh, Mandi Khajeh .
FUZZY SETS AND SYSTEMS, 2017, 314 :24-45
[3]   Dynamic analysis, controlling chaos and chaotification of a SMIB power system [J].
Chen, HK ;
Lin, TN ;
Chen, JH .
CHAOS SOLITONS & FRACTALS, 2005, 24 (05) :1307-1315
[4]   Disturbance-Observer-Based Robust Synchronization Control for a Class of Fractional-Order Chaotic Systems [J].
Chen, Mou ;
Shao, Shu-Yi ;
Shi, Peng ;
Shi, Yan .
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS, 2017, 64 (04) :417-421
[5]   Quantized-Feedback-Based Adaptive Event-Triggered Control of a Class of Uncertain Nonlinear Systems [J].
Choi, Yun Ho ;
Yoo, Sung Jin .
MATHEMATICS, 2020, 8 (09)
[6]   Adaptive Fuzzy Command Filtered Finite-Time Tracking Control for Uncertain Nonlinear Multi-Agent Systems with Unknown Input Saturation and Unknown Control Directions [J].
Deng, Xiongfeng ;
Huang, Yiqing ;
Wei, Lisheng .
MATHEMATICS, 2022, 10 (24)
[7]   Adaptive Neural Tracking Control for Nonstrict-Feedback Nonlinear Systems with Unknown Control Gains via Dynamic Surface Control Method [J].
Deng, Xiongfeng ;
Yuan, Yiming ;
Wei, Lisheng ;
Xu, Binzi ;
Tao, Liang .
MATHEMATICS, 2022, 10 (14)
[8]   Adaptive neural control of nonlinear time-delay systems with unknown virtual control coefficients [J].
Ge, SZS ;
Hong, F ;
Lee, TH .
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 2004, 34 (01) :499-516
[9]   Output feedback NN tracking control for fractional-order nonlinear systems with time-delay and input quantization [J].
Hua, Changchun ;
Ning, Jinghua ;
Zhao, Guanglei ;
Li, Yafeng .
NEUROCOMPUTING, 2018, 290 :229-237
[10]   Impulsive Control and Synchronization for Fractional-Order Hyper-Chaotic Financial System [J].
Li, Xinggui ;
Rao, Ruofeng ;
Zhong, Shouming ;
Yang, Xinsong ;
Li, Hu ;
Zhang, Yulin .
MATHEMATICS, 2022, 10 (15)