Adaptive neural network asymptotic tracking control for nonstrict feedback stochastic nonlinear systems

作     者：Liu, Yongchao Zhu, Qidan 

作者机构：Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin 150001 Peoples R China Harbin Engn Univ Key Lab Intelligent Technol & Applicat Marine Equ Minist Educ Harbin 150001 Peoples R China 

出 版 物：《NEURAL NETWORKS》 (神经网络)

年 卷 期：2021年第143卷

页      面：283-290页

核心收录：

学科分类：1002[医学-临床医学] 1001[医学-基础医学(可授医学、理学学位)] 0812[工学-计算机科学与技术（可授工学、理学学位）] 10[医学] 

基　　金：Development Project of Ship Situational Intelligent Awareness System, China [MC-201920-X01] National Natural Science Foundation of China 

主　　题：Asymptotic tracking control Backstepping algorithm Neural network Stochastic nonlinear systems 

摘      要：The adaptive neural network asymptotic tracking control issue of nonstrict feedback stochastic nonlinear systems is studied in our article by adopting backstepping algorithm. Compared with the existing research, the hypothesis about unknown virtual control coefficients (UVCC) is overcome in the control design. By using the bound estimation scheme and some smooth functions, associating with approximation-based neural network, the asymptotic tracking controller is recursively constructed. With the aid of Lyapunov function and beneficial inequalities, the asymptotic convergence character and stability with stochastic disturbance and unknown UVCC can be ensured. Finally, the theoretical finding is verified via a simulation example. (C) 2021 Published by Elsevier Ltd.