一种用于动态化工过程建模的反馈神经网络新结构

提出了一种新的用于非线性动态化工过程的状态集成反馈神经网络结构 (SIRNN) ,并将静态BP网络的训练算法引入到该网络的训练中 .状态反馈、时间序列延迟与集成节点的概念结合在SIRNN结构中 ,使得在用SIRNN建模过程中既可以考虑系统过去更多时刻的状态信息又可以相对降低网络的复杂程度 ,使得网络结构更趋于合理 .将SIRNN对一单输入单输出二阶非线性动态系统建模 ,并与其他反馈神经网络建模效果进行了比较 ,同时对该网络结构进行了抗干扰性检验 ,并对其在多输入单输出系统的应用中进行了尝试 ,结果表明SIRNN结构对非线性动态系统建模具有快速、高效和抗干扰的良好性能     

一种基于时序误差补偿的动态软测量建模方法

针对目前静态软测量建模方法无法反映工业过程动态信息,造成预测模型精度低、鲁棒性差等问题,提出了一种基于最小二乘支持向量机(LS-SVM)和自回归-滑动平均模型(ARMA)的软测量建模方法。首先,建立了基于LS-SVM的软测量模型,利用ARMA模型对预测误差的动态估计,通过增加动态校正环节,实现了对静态模型的动态校正以改善系统动态响应特性。最后将上述方法用于乙烯精馏过程中乙烷浓度的软测量建模,仿真结果表明:与单一使用LSSVM模型相比,该方法具有跟踪性能好、泛化能力强等优点,是一种有效的软测量建模方法。     

Dynamic gray‐box modeling for on‐line monitoring of polymer extrusion viscosity

Melt viscosity is a key indicator of product quality in polymer extrusion processes. However, real time monitoring and control of viscosity is difficult to achieve. In this article, a novel “soft sensor” approach based on dynamic gray‐box modeling is proposed. The soft sensor involves a nonlinear finite impulse response model with adaptable linear parameters for real‐time prediction of the melt viscosity based on the process inputs; the model output is then used as an input of a model with a simple‐fixed structure to predict the barrel pressure which can be measured online. Finally, the predicted pressure is compared to the measured value and the corresponding error is used as a feedback signal to correct the viscosity estimate. This novel feedback structure enables the online adaptability of the viscosity model in response to modeling errors and disturbances, hence producing a reliable viscosity estimate. The experimental results on different material/die/extruder confirm the effectiveness of the proposed “soft sensor” method based on dynamic gray‐box modeling for real‐time monitoring and control of polymer extrusion processes. POLYM. ENG. SCI., 2012. © 2012 Society of Plastics Engineers     

Multi-loop nonlinear internal model controller design based on a dynamic fuzzy partial least squares model

In this paper, a dynamic fuzzy partial least squares (DFPLS) modeling method is proposed. Under such framework, the multiple input multiple output (MIMO) nonlinear system can be automatically decomposed into several univariate subsystems in PLS latent space. Within each latent space, a dynamic fuzzy method is introduced to model the inherent dynamic and nonlinear feature of the physical system. The new modeling method combines the decoupling characteristic of PLS framework and the ability of dynamic nonlinear modeling in the fuzzy method. Based on the DFPLS model, a multi-loop nonlinear internal model control (IMC) strategy is proposed. A pH neutralization process and a methylcyclohexane (MCH) distillation column from Aspen Dynamic Module are presented to demonstrate the effectiveness of the proposed modeling method and control strategy.     

融合过程先验知识的递归神经网络模型及其应用

大部分化工过程具有非线性特性,一般的线性建模方法难以有效应用。针对非线性化工过程动态建模,提出了一种基于过程先验知识的递归神经网络模型,充分发掘化工过程隐含的先验知识,并将这些先验知识以非线性约束的形式嵌入NARMAX结构的前馈神经网络中,同时基于增广拉格朗日乘子法约束处理机制,用PSO-IPOPT混合优化算法对过程先验知识递归神经网络权值进行优化。该过程先验知识递归神经网络模型对非线性化工过程动态建模,不仅有良好的建模精度和预测外推能力,而且能避免零增益的出现和增益反转,确保网络模型在实际应用中的安全性。文中以环管式丙烯聚合反应过程实际工业数据验证了所提网络模型的有效性。     

High Moment Partial Sum Processes of Residuals in ARMA Models and their Applications

Abstract. In this article, we study high moment partial sum processes based on residuals of a stationary autoregressive moving average (ARMA) model with known or unknown mean parameter. We show that they can be approximated in probability by the analogous processes which are obtained from the i.i.d. errors of the ARMA model. However, if a unknown mean parameter is used, there will be an additional term that depends on model parameters and a mean estimator. When properly normalized, this additional term will vanish. Thus the processes converge weakly to the same Gaussian processes as if the residuals were i.i.d. Applications to change‐point problems and goodness‐of‐fit are considered, in particular, cumulative sum statistics for testing ARMA model structure changes and the Jarque–Bera omnibus statistic for testing normality of the unobservable error distribution of an ARMA model.     

基于ARX-NNPLS模型的优化策略及在聚合物牌号切换过程中的应用(英文)

Since it is often difficult to build differential algebraic equations (DAEs) for chemical processes, a new data-based modeling approach is proposed using ARX (AutoRegressive with eXogenous inputs) combined with neural network under partial least squares framework (ARX-NNPLS), in which less specific knowledge of the process is required but the input and output data. To represent the dynamic and nonlinear behavior of the process, the ARX combined with neural network is used in the partial least squares (PLS) inner model between input and output latent variables. In the proposed dynamic optimization strategy based on the ARX-NNPLS model, neither parameterization nor iterative solving process for DAEs is needed as the ARX-NNPLS model gives a proper representation for the dynamic behavior of the process, and the computing time is greatly reduced compared to conventional control vector parameterization method. To demonstrate the ARX-NNPLS model based optimization strategy, the polyethylene grade transition in gas phase fluidized-bed reactor is taken into account. The optimization results show that the final optimal trajectory of quality index determined by the new approach moves faster to the target values and the computing time is much less.     

Modeling and predictive control using fuzzy logic: Application for a polymerization system

In this study, a predictive control system based on type Takagi‐Sugeno fuzzy models was developed for a polymerization process. Such processes typically have a highly nonlinear dynamic behavior causing the performance of controllers based on conventional internal models to be poor or to require considerable effort in controller tuning. The copolymerization of methyl methacrylate with vinyl acetate was considered for analysis of the performance of the proposed control system. A nonlinear mathematical model which describes the reaction plant was used for data generation and implementation of the controller. The modeling using the fuzzy approach showed an excellent capacity for output prediction as a function of dynamic data input. The performance of the projected control system and dynamic matrix control for regulatory and servo problems were compared and the obtained results showed that the control system design is robust, of simple implementation and provides a better response than conventional predictive control. © 2009 American Institute of Chemical Engineers AIChE J, 2010     

一类非线性系统的变负荷预测控制

生产过程的变负荷运行使得其非线性动态特性的影响凸显。针对变负荷生产过程中机理模型为常微分方程或半显式Heisenberg微分-代数方程的一类非线性动态系统,采用非线性预测控制算法,构造出稳态优化与动态优化的两层控制结构,并采用联立法进行优化数值求解。最后对化工过程的夹套CSTR进行仿真验证,表明该算法的有效性。     

Polynomial ARMA model identification for a continuous styrene polymerization reactor using on‐line measurements of polymer properties

The multiinput–multioutput identification for a continuous styrene polymerization reactor using a polynomial ARMA model is carried out by both simulation and experiment. The pseudorandom multilevel input signals are applied for model identification in which input variables are the jacket inlet temperature and the feed flow rate, whereas the output variables are the monomer conversion and the weight‐average molecular weight. The use of a polynomial ARMA model for identification of the multivariable polymerization reaction system is validated by simulation study. For the experimental corroboration, correlations are developed to convert the on‐line measurements of density and viscosity of the reaction mixture to the monomer conversion and the weight‐average molecular weight. The on‐line values of the conversion and weight‐average molecular weight turn out to be in good agreement with the off‐line measurements. Despite the complex and nonlinear features of the polymerization reaction system, the polynomial ARMA model is found to satisfactorily describe the dynamic behavior of the polymerization reactor. Therefore, one may apply the polynomial ARMA model to the optimization and control of polymerization reactor systems. © 2000 John Wiley & Sons, Inc. J Appl Polym Sci 76: 1889–1901, 2000     

Nonlinear Model Algorithmic Control of a pH Neutralization Process

Control of pH neutralization processes is challenging in the chemical process industry because of their inherent strong nonlinearity. In this paper, the model algorithmic control （MAC） strategy is extended to nonlinear processes using Hammerstein model that consists of a static nonlinear polynomial function followed in series by a linear impulse response dynamic element. A new nonlinear Hammerstein MAC algorithm （named NLH-MAC） is presented in detail. The simulation control results of a pH neutralization process show that NLH-MAC gives better control performance than linear MAC and the commonly used industrial nonlinear propotional plus integral plus derivative （PID） controller. Further simulation experiment demonstrates that NLH-MAC not only gives good control response, but also possesses good stability and robustness even with large modeling errors.     

Modeling and control of Wiener-type processes

We develop a simple relay feedback method to identify Wiener-type nonlinear processes. It separates the identification problem of the nonlinear static function from that of the linear dynamic subsystem to simplify the identification procedure significantly. Owing to the separation, the unmeasurable output of the linear dynamic subsystem can be obtained in a straightforward manner. Then, determining the model structure of the nonlinear static function becomes very simple and the estimates are robust to additive output noises. We can identify the whole activated region of the nonlinear static function as well as the ultimate information of the linear dynamic subsystem from only one relay feedback test. More information on the linear dynamic subsystem can be estimated by well-established linear system identification methods from additional tests. We use a nonlinear control strategy to compensate the nonlinear dynamics of the Wiener process so that the design parameters can be determined by usual tuning rules developed for linear processes and a high control performance can be achievable as in linear processes.     

Generalized autoregressive moving average models with GARCH errors

One of the important and widely used classes of models for non-Gaussian time series is the generalized autoregressive model average models (GARMA), which specifies an ARMA structure for the conditional mean process of the underlying time series. However, in many applications one often encounters conditional heteroskedasticity. In this article, we propose a new class of models, referred to as GARMA-GARCH models, that jointly specify both the conditional mean and conditional variance processes of a general non-Gaussian time series. Under the general modeling framework, we propose three specific models, as examples, for proportional time series, non-negative time series, and skewed and heavy-tailed financial time series. Maximum likelihood estimator (MLE) and quasi Gaussian MLE are used to estimate the parameters. Simulation studies and three applications are used to demonstrate the properties of the models and the estimation procedures.     

Improved system identification method for Hammerstein-Wiener processes

We propose a new system identification method for Hammerstein-Wiener processes, in which an input static nonlinear block, a linear dynamic block, and an output static nonlinear block are connected in a series. The proposed method can estimate the model parameters in a very simple way without solving the full-dimensional nonlinear optimization problem by activating the process with a specially designed test signal, composed of a relay feedback signal, a binary signal and a multi-step signal. The proposed method analytically identifies the output nonlinear static function and the input nonlinear static function from the relay signal and the multi-step signal, respectively. The linear dynamic subsystem is identified from the relay feedback signal and the binary signal with existing well-established linear system identification methods. We demonstrate with a simple example that the proposed method can be successfully applied to identify the Hammerstein-Wiener-type nonlinear process.     

利用组合B样条神经网络实现对间歇反应过程的动态建模

间歇反应过程具有强非线性、非稳态和反应时间固定等特点。利用间歇反应操作时间可预先确定的性质,提出一种新的组合B样条神经网络的建模方法。被控对象输出f（u,t）往往是操纵变量和时间的函数,新方法把这两类函数关系的模拟分别交由两个神经网络承担,以确定变化区域的时间变量作为B样条神经网络的输入,让其分担描述对象随时间变化的动态特性部分,而输出变量与操作变量间的关系则由另一B样条神经网络表示,两个神经网络的组合输出建立间歇反应器的非线性动态模型。它不仅能够简化每个网络的结构,减少权值参数和训练时间,更重要的是可以方便控制策略的求解。本文介绍了建模方法的设计过程,并应用于苯乙烯悬浮聚合间歇反应建模中,仿真实验研究了方法的有效性。还推导了基于该模型的优化控制策略的算法。     

ESTIMATION OF MULTIVARIATE TIME SERIES

Abstract. The algorithm proposed here is a multivariate generalization of a procedure discussed by Pearlman (1980) for calculating the exact likelihood of a univariate ARMA model. Ansley and Kohn (1983) have shown how the Kalman filter can be used to calculate the exact likelihood function when not all the observations are known. In Shea (1983) it is shown that this algorithm is much quicker than that of Ansley and Kohn (1983) for all ARMA models except an ARMA (2, 1) and a couple of low-order AR processes and therefore when we have no missing observations this algorithm should be used instead. The Fortran subroutine G13DCF in the NAG (1987) Library fits a vector ARMA model using an adaptation of this algorithm. Experience in the use of this routine suggests that having reasonably good initial estimates of the ARMA parameter matrices, and in particular the residual error covariance matrix, can not only substantially reduce the computing time but more important improve the convergence properties of the minimization procedure. We therefore propose a method of calculating initial estimates of the ARMA parameters which involves using a generalization of the concept of inverse cross covariances from the univariate to the multivariate case. Finally theory is put into practice with the fitting of a bivariate model to a couple of real-life time series.     

基于自适应模糊神经网络的非线性系统模型预测控制

针对非线性动态系统的控制问题,提出了一种基于自适应模糊神经网络(adaptive fuzzy neural network, AFNN)的模型预测控制(model predictive control, MPC)方法。首先,在离线建模阶段,AFNN采用规则自分裂技术产生初始模糊规则,采用改进的自适应LM学习算法优化网络参数;然后,在实时控制过程,AFNN根据系统输出和预测输出之间的误差调整网络参数,从而为MPC提供一个精确的预测模型;进一步,AFNN-MPC利用带有自适应学习率的梯度下降寻优算法求解优化问题,在线获取非线性控制量,并将其作用到动态系统实施控制。此外,给出了AFNN-MPC的收敛性和稳定性证明,以保证其在实际工程中的成功应用。最后,利用数值仿真和双CSTR过程进行实验验证。结果表明,AFNN-MPC能够取得优越的控制性能。     

Dynamic output feedback covariance control of stochastic dissipative partial differential equations

In this work, we develop a method for dynamic output feedback covariance control of the state covariance of linear dissipative stochastic partial differential equations (PDEs) using spatially distributed control actuation and sensing with noise. Such stochastic PDEs arise naturally in the modeling of surface height profile evolution in thin film growth and sputtering processes. We begin with the formulation of the stochastic PDE into a system of infinite stochastic ordinary differential equations (ODEs) by using modal decomposition. A finite-dimensional approximation is then obtained to capture the dominant mode contribution to the surface roughness profile (i.e., the covariance of the surface height profile). Subsequently, a state feedback controller and a Kalman-Bucy filter are designed on the basis of the finite-dimensional approximation. The dynamic output feedback covariance controller is subsequently obtained by combining the state feedback controller and the state estimator. The steady-state expected surface covariance under the dynamic output feedback controller is then estimated on the basis of the closed-loop finite-dimensional system. An analysis is performed to obtain a theoretical estimate of the expected surface covariance of the closed-loop infinite-dimensional system. Applications of the linear dynamic output feedback controller to both the linearized and the nonlinear stochastic Kuramoto-Sivashinsky equations (KSEs) are presented. Finally, nonlinear state feedback controller and nonlinear output feedback controller designs are also presented and applied to the nonlinear stochastic KSE.     

非线性Hammerstein模型预测控制策略及其在pH中和过程中的应用

所有实际工业过程都包含一定程度的非线性,如pH中和过程由于其本身的强非线性是工业过程控制中具有挑战性的难题,但至今为止仍缺乏有效的非线性控制方法。将基于差分方程模型的模型预测控制策略(model predictive control,MPC)推广到包含一个静态非线性多项式函数和一个线性差分方程动态环节的非线性Hammerstein系统,详细描述了基于静态非线性多项式函数的最优控制作用求解方法,提出了一套新的非线性Hammerstein MPC 控制策略(nonlinear Hammerstein predictive control,NLHPC)。pH中和过程控制仿真和控制实验表明,NLHPC的控制结果好于工业上常用的非线性 PID(nonlinear PID,NL-PID)控制器。     

Two-degree-of-freedom output feedback controllers for discrete-time nonlinear systems

A discrete-time, model-based output feedback control structure for nonlinear processes is developed in the present work. The structure makes use of a closed-loop observer, while at the same time it guarantees that the overall feedback controller possesses integral action. An algebraic transformation is applied on the observer states to insure that the input/output gain of the observer matches the model upon which the static state feedback control law is based. The resulting control algorithm is a two-degree-of-freedom control law, in the sense that the output and the set point are processed in different ways. The control structure is shown not only to have the same properties as the standard model-state feedback structure, but also that it emerges from a model algorithmic control framework. Finally, a simulation example using an exothermic CSTR operating at an open-loop unstable steady state is used to evaluate the closed-loop performance of the proposed method.