Cooperative Fault-Tolerant Control for Networks of Stochastic Nonlinear Systems With Nondifferential Saturation Nonlinearity

被引:69
作者
Liang, Hongjing [1 ]
Liu, Guangliang [1 ]
Huang, Tingwen [2 ]
Lam, Hak-Keung [3 ]
Wang, Bohui [4 ]
机构
[1] Bohai Univ, Coll Engn, Jinzhou 121013, Peoples R China
[2] Texas A&M Univ Qatar, Sci Program, Doha, Qatar
[3] Kings Coll London, Dept Informat, London WC2B 4BG, England
[4] Xidian Univ, Sch Aerosp Sci & Technol, Xian 710071, Peoples R China
来源
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS | 2022年 / 52卷 / 03期
基金
中国国家自然科学基金;
关键词
Nonlinear systems; Fuzzy control; Stochastic processes; Actuators; Fuzzy neural networks; Adaptive systems; Protocols; Actuator faults; communication topology; fuzzy neural networks (FNNs); input saturation; networks of stochastic nonlinear systems; LEADER-FOLLOWING CONSENSUS; MULTIAGENT SYSTEMS; TRACKING CONTROL; NEURAL-CONTROL; FEEDBACK; ALGORITHM; DESIGN;
D O I
10.1109/TSMC.2020.3020188
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 article addresses the cooperative fault-tolerant control problem for networks of stochastic nonlinear systems with actuator faults and input saturation. The fuzzy neural networks (FNNs) are employed to estimate the unknown functions and stochastic disturbance terms. To analyze the nondifferential saturation nonlinearity, a smooth nonlinear function of the control input signal is constructed to estimate the saturation function. A novel adaptive fault-tolerant control protocol is proposed by using backstepping design technique. By using the stochastic Lyapunov functional strategy, it is proved that all the followers' outputs eventually converge to a small neighborhood of the leader's output, and all the signals in the closed-loop systems are bounded in probability. Finally, the performance of the proposed control strategy is illustrated through simulation.
引用
收藏
页码:1362 / 1372
页数:11
相关论文
共 49 条
[1]  
Boskovic JD, 1998, P AMER CONTR CONF, P2455, DOI 10.1109/ACC.1998.703075
[2]   Observer and Adaptive Fuzzy Control Design for Nonlinear Strict-Feedback Systems With Unknown Virtual Control Coefficients [J].
Chen, Bing ;
Liu, Xiaoping ;
Lin, Chong .
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2018, 26 (03) :1732-1743
[3]   Adaptive Consensus of Multi-Agent Systems With Unknown Identical Control Directions Based on A Novel Nussbaum-Type Function [J].
Chen, Weisheng ;
Li, Xiaobo ;
Ren, Wei ;
Wen, Changyun .
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2014, 59 (07) :1887-1892
[4]   Consensus of Multiagent Systems With Relative State Saturations [J].
Chu, Hongjun ;
Yue, Dong ;
Gao, Lixin ;
Lai, Xiangjing .
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2021, 51 (04) :2391-2402
[5]   High-gain estimator and fault-tolerant design with application to a gas turbine dynamic system [J].
Gao, Zhiwei ;
Breikin, Timofei ;
Wang, Hong .
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2007, 15 (04) :740-753
[6]   Fuzzy control turns 50: 10 years later [J].
Guerra, Thierry M. ;
Sala, Antonio ;
Tanaka, Kazuo .
FUZZY SETS AND SYSTEMS, 2015, 281 :168-182
[7]   Time-Varying Delay Compensation for a Class of Nonlinear Control Systems Over Network via H∞ Adaptive Fuzzy Controller [J].
Hamdy, Mohamed ;
Abd-Elhaleem, Sameh ;
Fkirin, M. A. .
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2017, 47 (08) :2114-2124
[8]   Adaptive output-feedback tracking of stochastic nonlinear systems [J].
Ji, HB ;
Xi, HS .
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2006, 51 (02) :355-360
[9]   Control Design for Interval Type-2 Fuzzy Systems Under Imperfect Premise Matching [J].
Lam, H. K. ;
Li, Hongyi ;
Deters, Christian ;
Secco, E. L. ;
Wurdemann, Helge A. ;
Althoefer, Kaspar .
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2014, 61 (02) :956-968
[10]   Stability Analysis and Performance Design for Fuzzy-Model-Based Control System Under Imperfect Premise Matching [J].
Lam, H. K. ;
Narimani, Mohammad .
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2009, 17 (04) :949-961