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
[31]   Design of adaptive backstepping dynamic surface control method with RBF neural network for uncertain nonlinear system [J].
Shi, Xiaoyu ;
Cheng, Yuhua ;
Yin, Chun ;
Huang, Xuegang ;
Zhong, Shou-ming .
NEUROCOMPUTING, 2019, 330 :490-503
[32]   Event-Trigger-Based Finite-Time Fuzzy Adaptive Control for Stochastic Nonlinear System With Unmodeled Dynamics [J].
Sui, Shuai ;
Chen, C. L. Philip ;
Tong, Shaocheng .
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2021, 29 (07) :1914-1926
[33]   Observer-Based Adaptive Fuzzy Backstepping Dynamic Surface Control for a Class of MIMO Nonlinear Systems [J].
Tong, Shao-Cheng ;
Li, Yong-Ming ;
Feng, Gang ;
Li, Tie-Shan .
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 2011, 41 (04) :1124-1135
[34]   Fuzzy adaptive observer backstepping control for MIMO nonlinear systems [J].
Tong Shaocheng ;
Li Changying ;
Li Yongming .
FUZZY SETS AND SYSTEMS, 2009, 160 (19) :2755-2775
[35]   Finite-Time Adaptive Fuzzy Tracking Control Design for Nonlinear Systems [J].
Wang, Fang ;
Chen, Bing ;
Liu, Xiaoping ;
Lin, Chong .
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2018, 26 (03) :1207-1216
[36]   Adaptive Neural Network Finite-Time Output Feedback Control of Quantized Nonlinear Systems [J].
Wang, Fang ;
Chen, Bing ;
Lin, Chong ;
Zhang, Jing ;
Meng, Xinzhu .
IEEE TRANSACTIONS ON CYBERNETICS, 2018, 48 (06) :1839-1848
[37]   Finite-Time Adaptive Fuzzy Command Filtered Backstepping Control for a Class of Nonlinear Systems [J].
Wang, Huanqing ;
Kang, Shijia ;
Feng, Zhiguang .
INTERNATIONAL JOURNAL OF FUZZY SYSTEMS, 2019, 21 (08) :2575-2587
[38]   Adaptive preassigned finite-time stability of nonlinear systems with time-varying powers and full-state constraints [J].
Wu, You ;
Xie, Xue-Jun .
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, 2022, 32 (04) :2200-2211
[39]   Neural-network-based adaptive finite-time output constraint control for rigid spacecrafts [J].
Xie, Shuzong ;
Tao, Meiling ;
Chen, Qiang ;
Tao, Liang .
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, 2022, 32 (05) :2983-3000
[40]   Adaptive fuzzy dynamic surface control for induction motors with iron losses in electric vehicle drive systems via backstepping [J].
Yu, Jinpeng ;
Ma, Yumei ;
Yu, Haisheng ;
Lin, Chong .
INFORMATION SCIENCES, 2017, 376 :172-189