A novel network-based controller design for a class of stochastic nonlinear systems with multiple faults and full state constraints

被引:9
|
作者
Li, Na [1 ]
Han, Yu-Qun [1 ]
He, Wen-Jing [1 ]
Zhu, Shan-Liang [1 ,2 ]
机构
[1] Qingdao Univ Sci & Technol, Sch Math & Phys, Qingdao, Peoples R China
[2] Qingdao Univ Sci & Technol, Sch Math & Phys, Qingdao 266061, Peoples R China
关键词
Stochastic nonlinear systems; full state constraints; multiple faults; multi-dimensional Taylor networks; BARRIER LYAPUNOV FUNCTIONS; TRACKING CONTROL; TOLERANT CONTROL; NEURAL-CONTROL; OBSERVER;
D O I
10.1080/00207179.2022.2163297
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this paper, the control issue of adaptive fault-tolerant is studied for a class of stochastic nonlinear systems with multiple faults and full state constraints, with multiple faults including the actuator faults and the external system fault. The problem with full state constraints are solved by constructing a logarithmic barrier Lyapunov functions (BLFs). By integrating multi-dimensional Taylor network (MTN) technology into the backstepping process, a new adaptive MTN-based fault-tolerant controller is designed. On the basis of considering multiple faults, the proposed control strategy can ensure that all signals in the closed-loop system are semi-global ultimately uniformly bounded (SGUUB) in probability, and all states of the system are constrained within the given boundary. Finally, three simulation examples are given to illustrate the effectiveness and practicability of the proposed control strategy.
引用
收藏
页码:651 / 661
页数:11
相关论文
共 50 条
  • [21] Neural network control-based adaptive design for a class of DC motor systems with the full state constraints
    Bai, Rui
    NEUROCOMPUTING, 2015, 168 : 65 - 69
  • [22] Adaptive control of a class of stochastic nonlinear systems with full state constraints and input saturation using multi-dimensional Taylor network
    Han, Yu-Qun
    ASIAN JOURNAL OF CONTROL, 2022, 24 (04) : 1609 - 1621
  • [23] Adaptive control of a class of stochastic nonlinear systems with full state constraints and input saturation using multi-dimensional Taylor network
    Han, Yu-Qun
    Asian Journal of Control, 2022, 24 (04): : 1609 - 1621
  • [24] Neural Network Control-Based Adaptive Learning Design for Nonlinear Systems With Full-State Constraints
    Liu, Yan-Jun
    Li, Jing
    Tong, Shaocheng
    Chen, C. L. Philip
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2016, 27 (07) : 1562 - 1571
  • [25] Design of Adaptive Finite-Time Fault-Tolerant Controller for Stochastic Nonlinear Systems With Multiple Faults
    Wang, Ming-Xin
    Zhu, Shan-Liang
    Liu, Si-Min
    Du, Yang
    Han, Yu-Qun
    IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, 2023, 20 (04) : 2492 - 2502
  • [26] BLF-based adaptive DSC for a class of stochastic nonlinear systems of full state constraints with time delay and hysteresis input
    Fei Shen
    Xinjun Wang
    Xinghui Yin
    NEUROCOMPUTING, 2020, 386 : 244 - 256
  • [27] Adaptive critic neural network-based controller for nonlinear systems
    Jagannathan, S
    PROCEEDINGS OF THE 2002 IEEE INTERNATIONAL SYMPOSIUM ON INTELLIGENT CONTROL, 2002, : 303 - 308
  • [28] Neural network-based iterative optimal controller for nonlinear systems
    Li, Mingzhong
    Wang, Fuli
    Dongbei Daxue Xuebao/Journal of Northeastern University, 1998, 19 (02): : 191 - 194
  • [29] Fault detection for a class of network-based nonlinear systems with communication constraints and random packet dropouts
    Zhang, Dan
    Yu, Li
    Wang, Qing-Guo
    INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, 2011, 25 (10) : 876 - 898
  • [30] Neural Network-Based Adaptive Consensus Control for a Class of Nonaffine Nonlinear Multiagent Systems With Actuator Faults
    Qin, Jiahu
    Zhang, Gaosheng
    Zheng, Wei Xing
    Kang, Yu
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2019, 30 (12) : 3633 - 3644