基于多信号源的神经模糊Hammerstein-Wiener模型研究

贾立; 杨爱华; 邱铭森

doi:10.3724/SP.J.1004.2013.00690

基于多信号源的神经模糊Hammerstein-Wiener模型研究

doi: 10.3724/SP.J.1004.2013.00690

1.
上海大学机电工程与自动化学院上海市电站自动化技术重点实验室上海 200072, 中国;
2.
新加坡国立大学工程学院新加坡 119260, 新加坡

详细信息

通讯作者:
贾立

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出版历程
- 收稿日期: 2012-05-11
- 修回日期: 2012-10-12
- 刊出日期: 2013-05-20

Research on Multi-signal Based Neuro-fuzzy Hammerstein-Wiener Model

1.
Shanghai Key Laboratory of Power Station Automation Technology, Department of Automation, College of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200072, China;
2.
Faculty of Engineering, National University of Singapore, Singapore 119260, Singapore

摘要

摘要: 面对复杂工业过程控制的需求, 设计一种结合数据信息的特殊模型结构, 在保证控制系统有效性的前提下通过模型的结构来简化控制器的求解是亟待解决的问题. 为此, 本文提出一种基于多信号源的神经模糊Hammerstein-Wiener模型, 突破传统的迭代分离方法, 通过组合式多信号实现Hammerstein-Wiener模型中神经模糊非线性环节和线性环节的分离, 同时设计了神经模糊模型参数的非迭代优化算法, 将研究结果拓广到分段非线性系统,改善了模型的适用范围. 该算法保证了模型的预测精度,具有逼近较强非线性过程的能力. 在此基础上设计了基于神经模糊Hammerstein-Wiener模型的控制系统, 利用模型的特殊结构将非线性系统的控制问题简化为线性系统的控制问题, 采用简单的PID控制器便能达到较好的控制效果.仿真结果验证了上述方法的有效性.
- Hammerstein-Wiener模型, 神经模糊系统, 非线性系统, 信号分离
Abstract: In order to solve the control problem of complex systems, it is important to design a special structure model with data information to simplify the question of designing control system. Thus, a multi-signal based neuro-fuzzy Hammerstein-Wiener model is proposed, which breaks through the traditional iterative separation method. The separation of the neuro-fuzzy nonlinear and linear parts of the Hammerstein-Wiener model is realized by one kind of multi-signals. And a noniterative neuro-fuzzy optimization algorithm is designed to expand the research results to piecewise nonlinear system, which can be applied to much more nonlinear systems. This algorithm guarantees the precision of the model. Moreover, it has the ability of approximating strong nonlinearity. Furthermore, a neuro-fuzzy Hammerstein-Wiener model based control system is designed to simplify the control problem of the nonlinear system into the problem of linear system by using the special structure of the model. As a result, the traditional PID controller can get a better control result. Simulated results show the effectiveness of the method.
- Hammerstein-Wiener model, neuro-fuzzy systems, nonlinear systems, signal separation

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参考文献(1)

[1]

Liu Y, Bai E W. Iterative identification of Hammerstein systems. Automatica, 2007, 43(2): 346-354[2] Gui Wei-Hua, Song Hai-Ying, Yang Chun-Hua. Hammer-stein-Wiener model identified by least-squares-support-vector machine and its application. Control Theory and Applications, 2008, 25(3): 393-397(桂卫华, 宋海鹰, 阳春华. Hammerstein-Wiener模型最小二乘向量机辨识及其应用. 控制理论与应用, 2008, 25(3): 393-397)[3] Marzban H R, Tabrizidooz H R, Razzaghi M. A composite collocation method for the nonlinear mixed Volterra-Fredholm-Hammerstein integral equations. Communications in Nonlinear Science and Numerical Simulation, 2011, 16(3): 1186-1194[4] Chen H T, Hwang S H, Chang C T. Iterative identification of continuous-time Hammerstein and Wiener systems using a two-stage estimation algorithm. Industrial and Engineering Chemistry Research, 2009, 48(3): 1495-1510[5] Wang Feng, Xing Ke-Yi, Xu Xiao-Ping. Study on method for identification of Hammerstein model. Journal of System Simulation, 2011, 23(6): 1090-1092, 1136(王峰, 邢科义, 徐小平. 辨识Hammerstein模型方法研究. 系统仿真学报, 2011, 23(6): 1090-1092, 1136)[6] Ding Feng, Liu Jing-Fan, Xiao Yong-Song. Parameter estimation for a class of nonlinear systems. Control of Engineering China, 2011, 18(3): 373-376, 409 (丁峰, 刘景璠, 肖永松. 一类非线性系统的参数估计. 控制工程, 2011, 18(3): 373-376, 409)[7] Hou Zhong-Sheng, Xu Jian-Xin. On data-driven control theory: the state of the art and perspective. Acta Automatica Sinica, 2009, 35(6): 650-667(侯忠生, 许建新. 数据驱动控制理论及方法的回顾和展望. 自动化学报, 2009, 35(6): 650-667)[8] Bai E W. An optimal two-stage identification algorithm for Hammerstein-Wiener nonlinear systems. Automatica, 1998, 34(3): 333-338[9] Wang D Q, Ding F. Extended stochastic gradient identification algorithms for Hammerstein-Wiener ARMAX systems. Computers and Mathematics with Applications, 2008, 56(12): 3157-3164[10] Zhu Y C. Estimation of an N-L-N Hammerstein-Wiener model. Automatica, 2002, 38(9): 1607-1614[11] Crama P, Schoukens J. Hammerstein-Wiener system estimator initialization. Automatica, 2004, 40(9): 1543-1550[12] Park H C, Sung S W, Lee J. Modeling of Hammerstein-Wiener processes with special input test signals. Industrial and Engineering Chemistry Research, 2006, 45(3): 1029- 1038[13] Man Hong, Shao Cheng. Neural network predictive control of continuous stirred-tank reactor based on Hammerstein-Wiener model. CIESC Journal, 2011, 62(8): 2275-2280(满红, 邵诚. 基于Hammerstein-Wiener模型的连续搅拌反应釜神经网络预测控制. 化工学报, 2011, 62(8): 2275-2280)[14] Li Yan, Mao Zhi-Zhong, Wang Yan, Yuan Ping. Fuzzy predictive control of Hammerstein-Wiener nonlinear systems. Journal of Northeastern University (Natural Science), 2011, 32(3): 322-326(李妍, 毛志忠, 王琰, 袁平. Hammerstein-Wiener非线性系统的模糊预测控制. 东北大学学报(自然科学版), 2011, 32(3): 322- 326)[15] Li Yan, Mao Zhi-Zhong, Wang Yan, Yuan Ping, Jia Ming-Xing. Predictive control of Hammerstein-Wiener nonlinearity based on polytopic terminal region. Acta Automatica Sinica, 2011, 37(5): 629-638(李妍, 毛志忠, 王琰, 袁平, 贾明兴. 基于多面体终端域的Hammerstein-Wiener非线性预测控制. 自动化学报, 2011, 37(5): 629-638)[16] Wang Wei, Chai Tian-You, Zhao Li-Jie. Dynamic partial least squares modeling with recurrent neural networks of stable learning. Control Theory and Applications, 2012, 29(3): 337-341(王魏, 柴天佑, 赵立杰. 带有稳定学习的递归神经网络动态偏最小二乘建模. 控制理论与应用, 2012, 29(3): 337-341)[17] Jia L, Chiu M S, Ge S S. A noniterative neuro-fuzzy based identification method for Hammerstein processes. Journal of Process Control, 2005, 15(7): 749-761[18] Hahn J, Edgar T F. A Gramian based approach to nonlinearity quantification and model classification. Industrial and Engineering Chemistry Research, 2001, 40(24): 5724-5731

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