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wiener model identification and nonlinear model predictive control of a pH neutralization process based on Laguerre filters and least squares support vector machines

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Journal of Zhejiang University-Science C(Computers and Electronics) 2011年第1期12卷 25-35页

作者： Qing-chao WANG Jian-zhong ZHANG School of Energy Science and Engineering Harbin Institute of Technology Harbin 150001 China SINOPEC Safety Engineering Institute Qingdao 266071 China

This paper deals with wiener model based predictive control of a pH neutralization *** dynamic linear block of the wiener model is parameterized using Laguerre filters while the nonlinear block is constructed using least squares support vector machines (LSSVM).Input-output data from the first principle model of the pH neutralization process are used for the wiener model *** results show that the proposed wiener model has higher prediction accuracy than Laguerre-support vector regression (SVR) wiener models,Laguerre-polynomial wiener models,and linear Laguerre *** identified wiener model is used here for nonlinear model predictive control (NMPC) of the pH neutralization *** set-point tracking performance of the proposed NMPC is compared with those of the Laguerre-SVR wiener model based NMPC,Laguerre-polynomial wiener model based NMPC,and linear model predictive control (LMPC).Validation results show that the proposed NMPC outperforms the other three controllers.

关键词： wiener model Nonlinear model predictive control (NMPC) pH neutralization process Laguerre filters Least squares support vector machines (LSSVM)

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Soft Sensor model Derived from wiener model Structure: modeling and Identification

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Chinese Journal of Chemical Engineering 2014年第5期22卷 538-548页

作者：曹鹏飞罗雄麟 Research Institute of Automation China University of Petroleum

The processes of building dynamic and static relationships between secondary and primary variables are usually integrated in most of nonlinear dynamic soft sensor models. However, such integration limits the estimation accuracy of soft sensor models. wiener model effectively describes dynamic and static characteristics of a system with the structure of dynamic and static submodels in cascade. We propose a soft sensor model derived from wiener model structure, which is an extension of wiener model. Dynamic and static relationships between secondary and primary variables are built respectively to describe the dynamic and static characteristics of system. The feasibility of this model is verified. Then the expression of discrete model is derived for soft sensor system. Conjugate gradient algorithm is applied to identify the dynamic and static model parameters alternately. Corresponding update method for soft sensor system is also given. Case studies confirm the effectiveness of the proposed model, alternate identification algorithm, and update method.

关键词： soft sensor wiener model modeling alternate identification

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Predictive Control Based on wiener model for Inlet Pressure of Primary Dynamic Wave in Sulfuric Acid Production from Smelter Gas

Predictive Control Based on Wiener Model for Inlet Pressure ...

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第24届中国控制与决策会议

作者： Chunhua Chen, Bin Cui, Feng Yu, Jun Zhang School of Information Science and Engineering, Northeastern University, Shenyang 110004, China

The structural features and parameter identification of wiener model are firstly introduced in this paper, and then through the online solving method Newton-Rapshon, the GPC algorithm based on wiener model is applied to the primary dynamic wave inlet pressure control of Sulfuric Acid Industry. Simulation results show that the GPC algorithm based on wiener model can solve the inlet pressure control problem of primary dynamic wave very well. Considering the inlet pressure drastic change when the working condition switches, some methods are adopted and the control effect is improved fatherly.

关键词： wiener model generalized predictive control inlet pressure control of primary dynamic wave

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PSO and RBF Network-Based wiener model and Its Application to System Identification

PSO and RBF Network-Based Wiener Model and Its Application t...

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第24届中国控制与决策会议

作者： Ren Yanyan, Wang Dongfeng, Liu Changliang, Han Pu Hebei Engineering Research Center of Simulation & Optimized Control for Power Generation (North China Electric Power University), Baoding 071003

In this paper, a new kind of wiener model structure is introduced, which is realized by using the mapping function of neural networks. The model uses the linear dynamic neurons and a RBF network to express one wiener model’s dynamic linear part and static nonlinear part respectively. The parameter identification for the new wiener model adopts the unified identification method. The learning of parameters includes two cycles that the inner-cycle is executed by gradient training methods based on the BP thought and the outer-cycle uses the PSO （Particle Swarm Optimization） algorithm. The training method based on unified identification makes the new wiener model converge to the steady state along the expected direction with a small error in a short time. The wiener model is applied to the identification of the famous Box and Jenkins gas CO 2 density, and the simulation results show that the method proposed in this paper is effective.

关键词： Identification wiener model Dynamic Neuron RBF Network Particle Swarm Optimization (PSO) Algorithm

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Identification of wiener models with Binary-Valued Output Observations

Identification of Wiener Models with Binary-Valued Output Ob...

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第25届中国控制会议

作者： Zhao Yanlong ,Wang Leyi, Yin Gang, Zhang Jifeng Academy of Mathematics and Systems Science,Chinese Academy of Sciences,Beijing 100080 Department of Electrical and Computer Engineering,Wayne State University,Detroit,Michigan 48202,U. S. A. Department of Mathematics,Wayne State University,Detroit,Michigan 48202,U. S. A.

＜正＞This work focuses on system identification forwiener models,whose outputs are measured by binary sensors. It begins with the development of joint identifiability. Then, using periodic inputs, empirical distributions are used to construct identification algorithms. Convergence of the algorithms is established,and associated recursive algorithms are also developed.

关键词： Identification binary-valued observations wiener model joint identifiability.

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Least Squares Based Iterative Parameter Estimation for Output Nonlinear Systems with Piece-Wise Nonlinearities

Least Squares Based Iterative Parameter Estimation for Outpu...

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第三十届中国控制会议

作者： CHEN Jing~(1,3),ZHANG Yan~2,DING Feng~(3*) 1.Wuxi Professional College of Science and Technology,Wuxi,214028,P.R.China 2.Wuxi Institute of Technology,Wuxi,214121,P.R.China 3.School of Internet of Things Engineering,Jiangnan University,Wuxi,214122,P.R.China

＜正＞This paper presents a least squares based iterative parameter estimation algorithm for a class of output nonlinear systems with piece-wise nonlinearities（also called wiener systems with saturation and dead-zone nonlinearities）.A wiener nonlinear system can be transformed into a special quasi-linear model by using a decomposition *** proposed least squares algorithm can estimate all the parameters of the whole nonlinear systems from all the input-output *** illustrative example is included.

关键词： Saturation and Dead-zone Least squares Parameter estimation wiener model

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