A Fast Finite-Time Neural Network Control of Stochastic Nonlinear Systems

被引:24
|
作者
Wang, Fang [1 ]
You, Zhaoyang [1 ]
Liu, Zhi [2 ]
Chen, C. L. Philip [3 ,4 ]
机构
[1] Shandong Univ Sci & Technol, Coll Math & Syst Sci, Qingdao 266590, Peoples R China
[2] Guangdong Univ Technol, Fac Automat, Guangzhou 510006, Guangdong, Peoples R China
[3] South China Univ Technol, Sch Comp Sci & Engn, Guangzhou 510006, Guangdong, Peoples R China
[4] Dalian Maritime Univ, Nav Coll, Dalian 116026, Peoples R China
基金
中国国家自然科学基金;
关键词
Nonlinear systems; Stability criteria; Convergence; Artificial neural networks; Stochastic systems; Lyapunov methods; Control systems; Auxiliary function; fast finite-time stability; neural networks (NNs); stochastic nonlinear systems; OUTPUT-FEEDBACK CONTROL; ADAPTIVE STABILIZATION; MULTIAGENT SYSTEMS; CONSENSUS TRACKING; STABILITY;
D O I
10.1109/TNNLS.2022.3143655
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This article takes a fast finite-time control of stochastic nonlinear systems into account. The presence of unknown stochastic disturbance terms makes the traditional fast finite-time control approaches unavailable. To deal with this difficulty, by establishing an auxiliary function and using Jensen's inequality, in Lemma 6, a new criterion of fast finite-time stability is first established for the uncertain stochastic system. Based on the approximation ability of neural networks (NNs), an innovative fast finite-time strategy is put forward for stochastic nonlinear systems. Furthermore, by adopting the presented fast finite-time stability criterion, the stability of the stochastic systems is confirmed. Finally, two simulations are implemented to validate the feasibility of the presented NN control strategy.
引用
收藏
页码:7443 / 7452
页数:10
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