基于观测器的非严格反馈时滞非线性系统的神经网络自适应控制

刘祥; 童东兵; 陈巧玉

doi:10.21656/1000-0887.410325

基于观测器的非严格反馈时滞非线性系统的神经网络自适应控制

doi: 10.21656/1000-0887.410325

1.上海工程技术大学电子电气工程学院，上海 201620;
2. 上海工程技术大学数理与统计学院，上海 201620

基金项目:

上海市自然科学基金（20ZR1422400）

国家自然科学基金(61673257)；中国博士后科学基金(2019M661322)

详细信息

作者简介:
刘祥(1996—)，男，硕士生(E-mail: lxhycb1109@163.com);童东兵(1979—)，男，副教授，博士(通讯作者. E-mail: tongdb@sues.edu.cn).

通讯作者:
童东兵(1979—)，男，副教授，博士(通讯作者. E-mail: tongdb@sues.edu.cn).

中图分类号: O175.13
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- 文章访问数: 790
- HTML全文浏览量: 214
- PDF下载量: 67
- 被引次数: 0
出版历程
- 收稿日期: 2020-10-23
- 修回日期: 2020-12-04

Observer-Based Adaptive Neural Network Control for Nonstrict-Feedback Nonlinear Systems With Time Delays

1. School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, P.R.China;
2. School of Mathematics, Physics and Statistics, Shanghai University of Engineering Science, Shanghai 201620, P.R.China

Funds:

The National Natural Science Foundation of China(61673257)

摘要

摘要: 针对一类非严格反馈的时滞非线性系统, 研究了一类基于观测器的自适应神经网络控制问题.针对系统中存在未知状态变量的问题, 设计了一个状态观测器.利用反步法和径向基神经网络的逼近特性, 提出了一种自适应神经网络输出反馈控制方法.所设计的控制器保证了闭环系统中所有信号的半全局一致有界性.最后, 通过仿真验证了所提控制方法的有效性.
- 神经网络 /
- 观测器 /
- 非严格反馈 /
- 自适应控制
Abstract: An observer-based adaptive neural network control problem was investigated for a class of nonstrict-feedback nonlinear systems with time delays. A state observer was constructed to estimate unknown variables in nonlinear systems. With the approximation ability of RBF NNs and the backstepping technique, an adaptive neural network output feedback control approach was proposed. The designed controller ensures the semi-global uniform boundedness of all signals in the closed-loop system. Finally, the simulation example shows the effectiveness of the proposed control approach.
- neural network /
- observer /
- nonstrict feedback /
- adaptive control

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参考文献(17)

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[3]KANELLAKOPOULOS I, KOKOTOVIC P V, MORSE A S. Systematic design of adaptive controllers for feedback linearizable systems[J]. IEEE Transactions on Automatic Control,1991,36(3): 1241-1253.

[4]HE W, CHEN Y, YIN Z. Adaptive neural network control of an uncertain robot with full-state constraints[J]. IEEE Transactions on Cybernetics,2016,46(3): 620-629.

[5]LI Y, TONG S, LI T. Composite adaptive fuzzy output feedback control design for uncertain nonlinear strict-feedback systems with input saturation[J]. IEEE Transactions on Cybernetics,2015,45(10): 2299-2308.

[6]SUN W, GAO H, KAYNAK O. Adaptive backstepping control for active suspension systems with hard constraints[J]. IEEE/ASME Transactions on Mechatronics,2013,18(3): 1072-1079.

[7]ZHOU Z, TONG D, CHEN Q, et al. Adaptive NN control for nonlinear systems with uncertainty based on dynamic surface control[J]. Neurocomputing,2021,421: 161-172.

[8]WANG A, LIU L, QIU J, et al. Event-triggered robust adaptive fuzzy control for a class of nonlinear systems[J]. IEEE Transactions on Fuzzy Systems,2018,27(8): 1648-1658.

[9]WU C, LIU J, XIONG Y, et al. Observer-based adaptive fault-tolerant tracking control of nonlinear nonstrict-feedback systems[J]. IEEE Transactions on Neural Networks and Learning Systems,2018,29(7): 3022-3033.

[10]CHEN C L P, WEN G X, LIU Y J, et al. Observer-based adaptive backstepping consensus tracking control for high-order nonlinear semi-strict-feedback multiagent systems[J]. IEEE Transactions on Cybernetics,2016,46(7): 1591-1601.

[11]NIU B, LI H, ZHANG Z, et al. Adaptive neural-network-based dynamic surface control for stochastic interconnected nonlinear nonstrict-feedback systems with dead zone[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems,2019,49(7): 1386-1398.

[12]ZHAO X, WANG X, ZONG G, et al. Fuzzy-approximation-based adaptive output-feedback control for uncertain nonsmooth nonlinear systems[J]. IEEE Transactions on Fuzzy Systems,2018,26(6): 3847-3859.

[13]佟英浩, 童东兵, 陈巧玉, 等. 事件触发驱动的非线性系统有限时间状态估计器设计[J]. 应用数学和力学, 2020,41(6): 669-678.(TONG Yinghao, TONG Dongbing, CHEN Qiaoyu, et al. Design of a finite-time state estimator for nonlinear systems under event-triggered control[J]. Applied Mathematics and Mechanics,2020,41(6): 669-678.(in Chinese))

[14]TONG S C, LI Y M, LIU Y J. Observer-based adaptive neural networks control for large-scale interconnected systems with nonconstant control gains[J]. IEEE Transactions on Neural Networks and Learning Systems,2021,32(4): 1575-1585.

[15]李刚，孟增. 基于RBF神经网络模型的结构可靠度优化方法[J]. 应用数学和力学, 2014,35(11): 1271-1279.(LI Gang, MENG Zeng. Reliability-based design optimization with the RBF neural network model[J]. Applied Mathematics and Mechanics,2014,35(11): 1271-1279.(in Chinese))

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基于观测器的非严格反馈时滞非线性系统的神经网络自适应控制

doi: 10.21656/1000-0887.410325

作者简介:
刘祥(1996—)，男，硕士生(E-mail: lxhycb1109@163.com);童东兵(1979—)，男，副教授，博士(通讯作者. E-mail: tongdb@sues.edu.cn).

通讯作者:
童东兵(1979—)，男，副教授，博士(通讯作者. E-mail: tongdb@sues.edu.cn).

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Observer-Based Adaptive Neural Network Control for Nonstrict-Feedback Nonlinear Systems With Time Delays

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留言板

基于观测器的非严格反馈时滞非线性系统的神经网络自适应控制

doi: 10.21656/1000-0887.410325

作者简介: 刘祥(1996—)，男，硕士生(E-mail: lxhycb1109@163.com);童东兵(1979—)，男，副教授，博士(通讯作者. E-mail: tongdb@sues.edu.cn).

通讯作者: 童东兵(1979—)，男，副教授，博士(通讯作者. E-mail: tongdb@sues.edu.cn).

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出版历程

Observer-Based Adaptive Neural Network Control for Nonstrict-Feedback Nonlinear Systems With Time Delays

计量

出版历程

目录

作者简介:
刘祥(1996—)，男，硕士生(E-mail: lxhycb1109@163.com);童东兵(1979—)，男，副教授，博士(通讯作者. E-mail: tongdb@sues.edu.cn).

通讯作者:
童东兵(1979—)，男，副教授，博士(通讯作者. E-mail: tongdb@sues.edu.cn).