Observer-based finite-time adaptive fuzzy back-stepping control for MIMO coupled nonlinear systems

被引:4
作者
Wang, Chao [1 ]
Zhang, Cheng [1 ]
He, Dan [2 ]
Xiao, Jianliang [1 ]
Liu, Liyan [1 ]
机构
[1] Dalian Univ Technol, Sch Comp Engn, City Inst, Dalian 116000, Peoples R China
[2] Dalian Univ Finance & Econ, Sch Management, Dalian 116000, Peoples R China
关键词
coupled nonlinear systems; adaptive fuzzy logic system; extended state observer; back-stepping; finite time; DYNAMIC SURFACE CONTROL; BACKSTEPPING CONTROL; NEURAL-NETWORK; MOTION CONTROL; DESIGN; MOTORS;
D O I
10.3934/mbe.2022497
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
An attempt is made in this paper to devise a finite-time adaptive fuzzy back-stepping control scheme for a class of multi-input and multi-output (MIMO) coupled nonlinear systems with immeasurable states. In view of the uncertainty of the system, adaptive fuzzy logic systems (AFLSs) are used to approach the uncertainty of the system, and the unmeasured states of the system are estimated by the finite-time extend state observers (FT-ESOs), where the state of the observer is a sphere around the state of the system. The accuracy and efficiency of the control effect are ensured by combining the back-stepping and finite-time theory. It is proved that all the states of the closed-loop adaptive control system are semi-global practical finite-time stability (SGPFS) by the finite-time Lyapunov stability theorem, and the tracking errors of the system states converge to a tiny neighborhood of the origin in a finite time. The validity of this scheme is demonstrated by a simulation.
引用
收藏
页码:10637 / 10655
页数:19
相关论文
共 42 条
[1]   Neural-Network-Based Adaptive Backstepping Control With Application to Spacecraft Attitude Regulation [J].
Cao, Xibin ;
Shi, Peng ;
Li, Zhuoshi ;
Liu, Ming .
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2018, 29 (09) :4303-4313
[2]   Backstepping-Based Finite-Time Adaptive Fuzzy Control of Unknown Nonlinear Systems [J].
Chang, Chia-Wen ;
Hsu, Chun-Fei ;
Lee, Tsu-Tian .
INTERNATIONAL JOURNAL OF FUZZY SYSTEMS, 2018, 20 (08) :2545-2555
[3]   Adaptive Dynamic Surface Control for Uncertain Nonlinear Systems With Interval Type-2 Fuzzy Neural Networks [J].
Chang, Yeong-Hwa ;
Chan, Wei-Shou .
IEEE TRANSACTIONS ON CYBERNETICS, 2014, 44 (02) :293-304
[4]   Adaptive dynamic surface control of stochastic nonstrict-feedback constrained nonlinear systems with input and state unmodeled dynamics [J].
Chen, Penghao ;
Zhang, Tianping .
INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, 2020, 34 (10) :1405-1429
[5]   Adaptive Backstepping Fuzzy Control for Nonlinearly Parameterized Systems With Periodic Disturbances [J].
Chen, Weisheng ;
Jiao, Licheng ;
Li, Ruihong ;
Li, Jing .
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2010, 18 (04) :674-685
[6]   GLOBALLY DECENTRALIZED ADAPTIVE BACKSTEPPING NEURAL NETWORK TRACKING CONTROL FOR UNKNOWN NONLINEAR INTERCONNECTED SYSTEMS [J].
Chen, Weisheng ;
Li, Junmin .
ASIAN JOURNAL OF CONTROL, 2010, 12 (01) :96-102
[7]   Neural-network-based adaptive backstepping control for a class of unknown nonlinear time-delay systems with unknown input saturation [J].
Dastres, Hossein ;
Rezaie, Behrooz ;
Baigzadehnoe, Barmak .
NEUROCOMPUTING, 2020, 398 :131-152
[8]   Adaptive backstepping control for flexible-joint manipulator using interval type-2 fuzzy neural network approximator [J].
Dian, Songyi ;
Hu, Yi ;
Zhao, Tao ;
Han, Jixia .
NONLINEAR DYNAMICS, 2019, 97 (02) :1567-1580
[9]   Finite-time adaptive control for nonlinear systems with uncertain parameters based on the command filters [J].
Ding, Jiling ;
Zhang, Weihai .
INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, 2021, 35 (09) :1754-1767
[10]   Design of interval type-2 fuzzy sliding-mode controller [J].
Hsiao, Ming-Ying ;
Li, Tzuu-Hseng S. ;
Lee, J. -Z. ;
Chao, C. -H. ;
Tsai, S. -H. .
INFORMATION SCIENCES, 2008, 178 (06) :1696-1716