Adaptive Finite-Time Neural Control for a Class of Stochastic Nonlinear Systems With Known Hysteresis

被引:6
作者
Li, Zheng [1 ]
Wang, Fang [2 ]
Wang, Jianhui [3 ,4 ]
机构
[1] Shandong Univ Sci & Technol, Sch Math & Syst Sci, Qingdao 266590, Peoples R China
[2] Shandong Univ Sci & Technol, Coll Math & Syst Sci, Qingdao 266590, Peoples R China
[3] Guangdong Univ Technol, Coll Automat, Guangzhou 510006, Peoples R China
[4] Guangzhou Univ, Sch Mech & Elect Engn, Guangzhou 510006, Peoples R China
来源
IEEE ACCESS | 2020年 / 8卷
关键词
Hysteresis; Nonlinear systems; Adaptive systems; Stability criteria; Stochastic processes; Neural networks; Bouc-Wen hysteresis; stochastic system; adaptive neural control; finite-time mean square stability; OUTPUT-FEEDBACK CONTROL; STABILIZATION; STABILITY; TRACKING; DESIGN;
D O I
10.1109/ACCESS.2020.2974871
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This manuscript considers the finite time adaptive neural tracking control problem for a class of the nonstrict-feedback stochastic nonlinear systems with Bouc-Wen hysteresis input. During the design process, a Bouc-Wen model is first adopted to obtain the input hysteresis phenomenon. By fusion with the backstepping technique and the neural network approximation capability, the unknown nonlinearities are coped with, a constructive finite time adaptive neural network control strategy is proposed. Furthermore, the finite-time mean square stability of stochastic nonlinear systems is proved, and at last the effectiveness of the proposed control strategy is validated by the simulations.
引用
收藏
页码:123639 / 123648
页数:10
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