基于时变RBF神经网络的时变非线性系统控制器设计

doi:10.16182/j.issn1004731x.joss.23-FZ0796E

系统仿真学报 ›› 2023, Vol. 35 ›› Issue (10): 2223-2236.doi: 10.16182/j.issn1004731x.joss.23-FZ0796E

基于时变RBF神经网络的时变非线性系统控制器设计

李静(), 张涛涛, 金凯(), 袁胜智, 查子龙

海军工程大学兵器工程学院，湖北武汉 430033

收稿日期:2023-07-02 修回日期:2023-08-14 出版日期:2023-10-30 发布日期:2023-10-26
通讯作者: 金凯 E-mail:Lijing7292013@163.com;wh2023wh@outlook.com

Time-varying RBF Neural Network-based Controller Design for a Class of Time-varying Nonlinear Systems

Li Jing(), Zhang Taotao, Jin Kai(), Yuan Shengzhi, Zha Zilong

College of Weapons Engineering, Naval University of Engineering, WuHan 430033, China

Received:2023-07-02 Revised:2023-08-14 Online:2023-10-30 Published:2023-10-26
Contact: Jin Kai E-mail:Lijing7292013@163.com;wh2023wh@outlook.com
About author:Li Jing (1982-), male, lecturer, PhD, research areas: nonlinear control system, iterative learning control, artificial intelligence, etc. E-mail: Lijing7292013@163.com

摘要/Abstract

摘要：

提出了一种具有时变特性的RBF神经网络，并证明了其逼近定理。以一类非参数化不确定时变非线性系统为研究对象，在用时变RBF神经网络对系统中的时变不确定性进行逼近的同时，综合应用Lyapunov稳定性理论和自适应迭代学习控制技术，设计了控制系统，并得到了稳定性定理，解决了这类系统的控制问题。仿真结果验证了时变神经网络的有效性和控制器设计方案的正确性。

关键词: 时变非线性, RBF神经网络, 迭代学习, 自适应控制

Abstract:

Atime-varying RBF neural network with time-varying properties is firstly proposed, and its approximation theorem is obtained. For a class of nonlinear systems with non-parametric time-varying uncertainties, the proposed time-varying RBF neural network is used to approximate the time-varying uncertainties, and the controller is designed by making use of Lyapunov stability theory and adaptive iterative learning control techniques. We obtain the stability theorem of the designed controller. The simulation results verify the effectiveness of the time-varying neural network and the correctness of the controller design scheme.

Key words: time-varying nonlinearity, RBF neural network, iterative learning, adaptive control

中图分类号:

TP273

李静,张涛涛,金凯等 . 基于时变RBF神经网络的时变非线性系统控制器设计[J]. 系统仿真学报, 2023, 35(10): 2223-2236.

Li Jing,Zhang Taotao,Jin Kai,et al . Time-varying RBF Neural Network-based Controller Design for a Class of Time-varying Nonlinear Systems[J]. Journal of System Simulation, 2023, 35(10): 2223-2236.

图/表 6

参考文献 18

1	Zhang Jinxi, Yang Guanghong. Fuzzy Adaptive Fault-tolerant Control of Unknown Nonlinear Systems With Time-varying Structure[J]. IEEE Transactions on Fuzzy Systems, 2019, 27(10): 1904-1916.
2	Guo Chao, Xie Ruiming, Xie Xuejun. Adaptive Control of Full-state Constrained High-order Nonlinear Systems With Time-varying Powers[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021, 51(8): 5189-5197.
3	Mazenc Frédéric, Malisoff M. Stabilization of Nonlinear Time-varying Systems Through a New Prediction Based Approach[J]. IEEE Transactions on Automatic Control, 2017, 62(6): 2908-2915.
4	Chen Weisheng, Jiao Licheng. Adaptive Tracking for Periodically Time-varying and Nonlinearly Parameterized Systems Using Multilayer Neural Networks[J]. IEEE Transactions on Neural Networks, 2010, 21(2): 345-351.
5	Chien C J, Liu J S. A P-type Iterative Learning Controller for Robust Output Tracking of Nonlinear Time-varying Systems[J]. International Journal of Control, 1996, 64(2): 319-334.
6	皮道映, 孙优贤. 离散非线性系统开闭环P型迭代学习控制律及其收敛性[J]. 控制理论与应用, 1997, 14(2): 157-161.
	Pi Daoying, Sun Youxian. On the Convergence of Open-closed-loop P-type Iterative Learning Control Scheme for Nonlinear Discrete Systems[J]. Control Theory and Applications, 1997, 14(2): 156-161.
7	谢胜利, 谢振东, 田森平. 非线性系统的迭代学习控制及其算法实现[J]. 控制理论与应用, 2002, 19(2): 167-172, 177.
	Xie Shengli, Xie Zhendong, Tian Senping. Iterative Learning Control and Algorithm Realization of Nonlinear Systems[J]. Control Theory and Applications, 2002, 19(2): 167-172, 177.
8	Xu Jianxin, Qu Zhihua. Robust Iterative Learning Control for a Class of Nonlinear Systems[J]. Automatica, 1998, 34(8): 983-988.
9	Xu Jianxin, Viswanathan B. Adaptive Robust Iterative Learning Control With Dead zone Scheme[J]. Automatica, 2000, 36(1): 91-99.
10	孙云平, 李俊民, 王元亮. 一类高阶非线性参数化系统自适应重复学习控制[J]. 控制与决策, 2008, 23(11): 1286-1290.
	Sun Yunping, Li Junmin, Wang Yuanliang. Adaptive Repetitive Learning Control for a Class of Nonlinearly Parameterized Uncertain Systems[J]. Control and Decision, 2008, 23(11): 1286-1290.
11	Yan Qiuzhen, Cai Jianping, Zhang Yuntao, et al. Adaptive Iterative Learning Control for Robot Manipulators With Time-varying Parameters and Arbitrary Initial Errors[J]. IEEE Access, 2021, 9: 166920-166931.
12	Zhang Chunli, Li Junmin. Adaptive Iterative Learning Control of Non-uniform Trajectory Tracking for Strict Feedback Nonlinear Time-varying Systems With Unknown Control Direction[J]. Applied Mathematical Modelling, 2015, 39(10-11): 2942-2950.
13	Gu Panpan, Tian Senping. Analysis of Iterative Learning Control for One-sided Lipschitz Nonlinear Singular Systems[J]. Journal of the Franklin Institute, 2019, 356(1): 196-208.
14	Xu Jianxin, Tan Ying, Lee T H. Iterative Learning Control Design Based on Composite Energy Function With Input Saturation[C]//Proceedings of the 2003 American Control Conference. Piscataway, NJ, USA: IEEE, 2003: 5129-5134.
15	Wei Jianming, Zhang Youan, Sun Meimei, et al. Adaptive Iterative Learning Control of a Class of Nonlinear Time-delay Systems With Unknown Backlash-like Hysteresis Input and Control Direction[J]. ISA Transactions, 2017, 70: 79-92.
16	Tayebi A, Chien C J. A Unified Adaptive Iterative Learning Control Framework for Uncertain Nonlinear Systems[J]. IEEE Transactions on Automatic Control, 2007, 52(10): 1907-1913.
17	Chien C J, Wang Y C. Decentralized Adaptive Iterative Learning Control for Nonaffine Nonlinear Interconnected Systems[C]//2009 7th Asian Control Conference. Piscataway, NJ, USA: IEEE, 2009: 722-727.
18	Park J, Sandberg I W. Universal Approximation Using Radial-basis-function Networks[J]. Neural Computation, 1991, 3(2): 246-257.

[1]	张立峰, 苗雨. 一种声学层析成像温度分布高分辨率重建方法[J]. 系统仿真学报, 2022, 34(09): 2065-2073.
[2]	李中奇, 邢月霜. 动车组进站过程精准停车控制方法研究[J]. 系统仿真学报, 2021, 33(1): 149-158.
[3]	李琳, 林燕龙, 邹焱飚. Scara机器人关节摩擦建模与补偿的实验与仿真[J]. 系统仿真学报, 2019, 31(8): 1572-1581.
[4]	梁岗, 赵一鸣, 覃恒明. 基于迭代学习控制的岸桥小车定位控制仿真研究[J]. 系统仿真学报, 2018, 30(8): 3154-3160.
[5]	刘慧博, 刘木. 基于心率的神经网络最小参数血泵滑模控制[J]. 系统仿真学报, 2018, 30(5): 1796-1802.
[6]	时维国, 王力, 邵城. 资源受限系统的灰色神经网络变采样调度算法[J]. 系统仿真学报, 2018, 30(4): 1272-1278.
[7]	周颖, 陈阳, 岳彬. 改进的模型参考自适应在磨矿过程中的应用[J]. 系统仿真学报, 2018, 30(4): 1608-1614.
[8]	贺军, 骆敏舟, 赵江海, 徐林森, 李涛. 冗余度机器人变负载预测模糊自适应滑模控制[J]. 系统仿真学报, 2018, 30(3): 994-1001.
[9]	高洁, 高冰, 刘建英. 基于IL的飞机防滑刹车系统反馈补偿控制研究[J]. 系统仿真学报, 2017, 29(7): 1625-1630.
[10]	刘晓琳, 王春婷. 飞机舵机电液加载系统多余力抑制方法研究[J]. 系统仿真学报, 2017, 29(2): 409-417.
[11]	刘加存, 梅其祥, 杨东红. 两级学习目标的幂函数型神经网络建模[J]. 系统仿真学报, 2017, 29(1): 34-42.
[12]	陈兵, 黄媛媛, 尹忠俊. 输流管道振动自适应控制算法鲁棒特性研究[J]. 系统仿真学报, 2016, 28(6): 1445-1452.
[13]	巫庆辉, 申庆欢, 王新君. 基于RBF神经网络的离心式水泵模型研究[J]. 系统仿真学报, 2016, 28(4): 800-805.
[14]	吴云洁, 宋闯, 左京兴. 低空飞行时改进的自适应鲁棒滑模姿态控制器[J]. 系统仿真学报, 2015, 27(9): 2163-2168.
[15]	龙浩, 宋述杰. 具有自修复功能自适应飞行控制系统仿真研究[J]. 系统仿真学报, 2015, 27(11): 2791-2796.

基于时变RBF神经网络的时变非线性系统控制器设计

Time-varying RBF Neural Network-based Controller Design for a Class of Time-varying Nonlinear Systems

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 6

参考文献 18

相关文章 15

编辑推荐

Metrics

本文评价