Adaptive neural network output feedback control for stochastic nonlinear systems with full state constraints

被引：60

作者：

Zhu, Qidan ^{[1
,2
]}

Liu, Yongchao ^{[1
,2
]}

Wen, Guoxing ^{[3
]}

机构：

[1] Harbin Engn Univ, Coll Automat, Harbin 150001, Heilongjiang, Peoples R China

[2] Harbin Engn Univ, Minist Educ, Key Lab Intelligent Technol & Applicat Marine Equ, Harbin 150001, Heilongjiang, Peoples R China

[3] Binzhou Univ, Coll Sci, Binzhou 256600, Shandong, Peoples R China

来源：

ISA TRANSACTIONS | 2020年 / 101卷 / 101期

基金：

中国国家自然科学基金;

关键词：

Neural network; Output feedback; Full state constraints; Dynamic surface control; Stochastic nonlinear systems; BARRIER LYAPUNOV FUNCTIONS; BACKSTEPPING CONTROL; ROBUST; STABILIZATION; PERFORMANCE;

D O I：

10.1016/j.isatra.2020.01.021

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 paper presents an adaptive neural network output feedback control method for stochastic nonlinear systems with full state constraints. The barrier Lyapunov functions are used to conquer the effect of state constraints to system performance. The neural network state observer is established to estimate the unmeasured states. By using dynamic surface control technique, the "explosion of complexity'' issue existing in the backstepping design is overcome. The proposed control scheme can guarantee that all signals of the system are bounded and the system output can follow the desired signal. Finally, two examples are given to verify the effectiveness of our control method. (C) 2020 ISA. Published by Elsevier Ltd. All rights reserved.

引用

页码：60 / 68

页数：9

共 50 条

[1] Adaptive neural network control of nonlinear systems by state and output feedback
Ge, SS
Hang, CC
Zhang, T
[J]. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 1999, 29 (06): : 818 - 828
[2] Adaptive output feedback control for a class of nonlinear systems with full-state constraints
Liu, Yan-Jun
Li, Dong-Juan
Tong, Shaocheng
[J]. INTERNATIONAL JOURNAL OF CONTROL, 2014, 87 (02) : 281 - 290
[3] Adaptive Fuzzy Output Feedback Control for a Class of Nonlinear Systems With Full State Constraints
Liu, Yan-Jun
Gong, Mingzhe
Tong, Shaocheng
Chen, C. L. Philip
Li, Dong-Juan
[J]. IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2018, 26 (05) : 2607 - 2617
[4] Neural network adaptive finite-time control of stochastic nonlinear systems with full state constraints
Zhu, Qidan
Liu, Yongchao
[J]. ASIAN JOURNAL OF CONTROL, 2021, 23 (04) : 1728 - 1739
[5] Adaptive neural network output feedback control of stochastic nonlinear systems with dynamical uncertainties
Wang, Tong
Tong, Shaocheng
Li, Yongming
[J]. NEURAL COMPUTING & APPLICATIONS, 2013, 23 (05): : 1481 - 1494
[6] Adaptive neural network output feedback control of stochastic nonlinear systems with dynamical uncertainties
Tong Wang
Shaocheng Tong
Yongming Li
[J]. Neural Computing and Applications, 2013, 23 : 1481 - 1494
[7] Adaptive Fuzzy Output Feedback Control for A Class of Nonlinear Pure-Feedback Systems with Full State Constraints
Yi, Jiale
Li, Jing
Li, Junmin
Wu, Jian
[J]. PROCEEDINGS OF THE 36TH CHINESE CONTROL CONFERENCE (CCC 2017), 2017, : 862 - 867
[8] Adaptive neural optimal tracking control of stochastic nonstrict-feedback nonlinear systems with output constraints
Chen, Penghao
Luan, Xiaoli
Wang, Zhiguo
Zhang, Tianping
Ge, Yuan
Liu, Fei
[J]. JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 2023, 360 (16): : 12299 - 12338
[9] Adaptive neural network asymptotic tracking control for a class of stochastic nonlinear systems with unknown control gains and full state constraints
Su, Wei
Niu, Ben
Wang, Huanqing
Qi, Wenhai
[J]. INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, 2021, 35 (10) : 2007 - 2024
[10] Adaptive neural control for MIMO stochastic nonlinear pure-feedback systems with input saturation and full-state constraints
Si, Wenjie
Dong, Xunde
[J]. NEUROCOMPUTING, 2018, 275 : 298 - 307

← 1 2 3 4 5 →