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Investigation of Dynamic Multivariate Chemical process Monitoring

Chinese Journal of Chemical Engineering 2006年第5期14卷 559-568页

作者：谢磊张建明王树青 National Key Laboratory of Industrial Control Technology Institute of Advanced Process Control Zhejiang Uni-versity

Chemical process variables are always driven by random noise and disturbances. The closed-loop con-trol yields process measurements that are auto and cross correlated. The influence of auto and cross correlations on statistical process control (SPC) is investigated in detail by Monte Carlo experiments. It is revealed that in the sense of average performance, the false alarms rates (FAR) of principal component analysis (PCA), dynamic PCA are not affected by the time-series structures of process variables. Nevertheless, non-independent identical distribution will cause the actual FAR to deviate from its theoretic value apparently and result in unexpected consecutive false alarms for normal operating process. Dynamic PCA and ARMA-PCA are demonstrated to be inefficient to remove the influences of auto and cross correlations. Subspace identification-based PCA (SI-PCA) is proposed to improve the monitoring of dynamic processes. Through state space modeling, SI-PCA can remove the auto and cross corre-lations efficiently and avoid consecutive false alarms. Synthetic Monte Carlo experiments and the application in Tennessee Eastman challenge process illustrate the advantages of the proposed approach.

关键词： multivariate statistical processes control subspace identification false alarms rate dynamic processes

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Fast algorithm for adaptive multivariable generalized predictive control

Fast algorithm for adaptive multivariable generalized predic...

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IECON 2006 - 32nd Annual Conference on IEEE industrial Electronics

作者： Li, Qi-An National Key Laboratory of Industrial Control Technology Institute of Advanced Process Control Zhejiang University Hangzhou 310027 China

ISBN: (纸本)1424401364

A very concise method is presented to simplify the implementation of adaptive muldvariable generalized predictive control (MGPC). If a physically realizable multivariable process can be described by a controlled auto-regressive integrated moving average (CARIMA) model with diagonal matrices C and A, the way to get MGPC controller coefficients can be simplified, since there exists direct expressions describing the nonlinear relationship between the open-loop model parameters and the MGPC controller coefficients according to a certain set of tuning parameters. The control moves are just the product of the process known information and the MGPC controller coefficients. Then a multilayer feedforward neural network is trained to obtain the controller coefficients from model parameters quickly, which substantially abates the mathematical computational overhead associated with MGPC. The feasibility and efficiency of this algorithm is demonstrated by comparison experiment results. © 2006 IEEE.

关键词： controllers

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Swarm intelligent tuning of one-class v-SVM parameters

Swarm intelligent tuning of one-class v-SVM parameters

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First International Conference on Rough Sets and Knowledge Technology, RSKT 2006

作者： Xie, Lei National Key Laboratory of Industrial Control Technology Institute of Advanced Process Control Zhejiang University Hangzhou 310027 China

ISBN: (纸本)3540362975

The problem of kernel parameters selection for one-class classifier, v-SVM, is studied. An improved constrained particle swarm optimization (PSO) is proposed to optimize the RBF kernel parameters of the v-SVM and two kinds of flexible RBF kernels are introduced. As a general purpose swarm intelligent and global optimization tool, PSO do not need the classifier performance criterion to be differentiable and convex. In order to handle the parameter constraints involved by the v-SVM, the improved constrained PSO utilizes the punishment term to provide the constraints violation information. Application studies on an artificial banana dataset the efficiency of the proposed method. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Learning systems

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Fault Isolation by Partial Dynamic Principal Component Analysis in Dynamic process

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Chinese Journal of Chemical Engineering 2006年第4期14卷 486-493页

作者：李荣雨荣冈 National Key Laboratory of Industrial Control Technology Institute of Advanced Process Control Zhejiang Uni-versity Hangzhou 310027 China

Principal component analysis (PCA) is a useful tool in process fault detection, but offers little support on fault isolation. In this article, structured residual with strong isolation property is introduced. Although it is easy to get the residual by transformation matrix in static process, unfortunately, it becomes hard in dynamic process under control loop. Therefore, partial dynamic PCA(PDPCA) is proposed to obtain structured residual and enhance the isolation ability of dynamic process monitoring, and a compound statistic is introduced to resolve the problem resulting from independent variables in every variable subset. Simulations on continuous stirred tank reactor (CSTR) show the effectiveness of the proposed method.

关键词： fault isolation structured residual dynamic principal component analysis partial principal component

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P systems based multi-objective optimization algorithm

P systems based multi-objective optimization algorithm

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1st International Conference on Bio-Inspired Computing - Theory and Applications, BIC-TA 2006

作者： Huang, Liang He, Xiong-Xiong Wang, Ning Xie, Yi National Laboratory of Industrial Control Technology Institute of Advanced Process Control Zhejiang University Hangzhou 310027 China Zhejiang University of Technology Hangzhou 310014 China

Based on P systems, this paper proposes a new multi-objective optimization algorithm (PMOA). Similar to P systems, PMOA has a cell-like structure. Their structure is dynamic and its membranes merge and divide at different stages. The key rule of a membrane is the communication rule which is derived from P systems. Mutation rules are important for the algorithm, which has different ranges of mutation in different membranes. The cooperation of the two rules contributes to the diversity of the population, the conquest of the multimodal of objective function and the convergence of algorithm. Moreover, the unique structure divides the whole population into several subpopulations, which decrease the computational complexity. Almost a dozen popular algorithms are compared using several test problems. Simulation results illustrate that the PMOA has the best performance. Its solutions are closer to the true Pareto-optimal front and distribute well. Moreover, it converges fast.

关键词： Multiobjective optimization

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On the Dual of a Mixed H_2/l_1 Optimisation Problem

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International Journal of Automation and computing 2006年第1期3卷 91-98页

作者： Jun Wu Jian Chu Sheng Chen National Key Laboratory of Industrial Control TechnologyInstitute of Advanced Process Control Zhejiang University Hangzhou 310027 PRC School of Electronics and Computer Science University of Southampton Highfield Southampton SO17 IBJ UK

The general discrete-time Single-Input Single-Output （SISO） mixed H2/l1 control problem is considered in this paper. It is found that the existing results of duality theory cannot be directly applied to this infinite dimension optimisation problem. By means of two finite dimension approximate problems, to which duality theory can be applied, the dual of the mixed H2/l1 control problem is verified to be the limit of the duals of these two approximate problems.

关键词： Optimal control mixed H2/l1 control duality theory.

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Predictive control of a Class of Bilinear Systems Based on Global off-line Models

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Journal of Zhejiang University-Science B(Biomedicine & Biotechnology) 2006年第12期7卷 1984-1988页

作者： Ri-Dong Zhang Shu-Qing Wang Institute of Advanced Process Control National Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou 310027 China

A new multi-step adaptive predictive control algorithm for a class of bilinear systems is presented. The structure of the bilinear system is converted into a simple linear model by using nonlinear support vector machine (SVM) dynamic approximation with analytical control law derived. The method does not need on-line parameters estimation because the system's internal model has been transformed into an off-line global model. Compared with other traditional methods, this control law reduces on-line parameter estimating burden. In addition, its overall linear behavior treating method allows an analytical control law available and avoids on-line nonlinear optimization. Simulation results are presented in the article to illustrate the efficiency of the method.

关键词： Bilinear systems Model predictive control MPC Adaptive control Support vector machine SVM

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Delay-dependent robust stability of uncertain discrete singular time-delay systems

Delay-dependent robust stability of uncertain discrete singu...

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American control Conference (ACC)

作者： Xiaofu Ji Hongye Su Jian Chu Institute of Advanced Process Control National Key Laboratory of Industrial Control Technology Hangzhou China

This paper deals with the problem of delay-dependent robust stability of a class of uncertain discrete singular time-delay systems. The considered systems are subject to norm-bounded parameter uncertainties and constant time delay. A new approach is introduced to take the relationship between the fast and slow subsystems of a discrete singular time-delay system, based on which, a strict linear matrix inequality (LMI) criterion is obtained for a discrete singular time-delay system to be regular, causal and stable. The result on robust stability of uncertain discrete singular time-delay systems is also obtained and expressed in terms of LMIs. Numerical examples are given to demonstrate the applicability of the proposed method

关键词： Robust stability Linear matrix inequalities Symmetric matrices Delay effects Uncertain systems Delay systems Power system dynamics Difference equations Matrix decomposition Delay lines

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Flowsheet Simulation Research in Soda Production

Flowsheet Simulation Research in Soda Production

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World Congress on Intelligent control and Automation (WCICA)

作者： Huabing Wu Gang Rong National Key Laboratory of Industrial Control Technology Industrial of Advanced Process Control University of Zhejiang Hangzhou China

According to the real soda production and the material, energy balance, a set of flowsheet simulation platform is developed on the basis of the MATLAB/SIMULINK. Virtual production plants, instruments and pipes are setting up in the platform. The platform can simulate the real production and export real data of every device to the database, also virtual instruments can be built, through added different kinds of errors, the measuring data with error are obtained. Thus the platform can become the data source for investigation of data reconciliation and process optimization

关键词： Production MATLAB Instruments Laboratories industrial control process control Databases Error correction Intelligent control Automation

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Fast Algorithm for Adaptive Multivariable Generalized Predictive control

Fast Algorithm for Adaptive Multivariable Generalized Predic...

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Annual Conference of industrial Electronics Society

作者： Qi-An Li National Key Laboratory of Industrial Control Technology Institute of Advanced Process Control University of Zhejiang Hangzhou China

A very concise method is presented to simplify the implementation of adaptive multivariable generalized predictive control (MGPC). If a physically realizable multivariable process can be described by a controlled auto-regressive integrated moving average (CARIMA) model with diagonal matrices C and A, the way to get MGPC controller coefficients can be simplified, since there exists direct expressions describing the nonlinear relationship between the open-loop model parameters and the MGPC controller coefficients according to a certain set of tuning parameters. The control moves are just the product of the process known information and the MGPC controller coefficients. Then a multilayer feedforward neural network is trained to obtain the controller coefficients from model parameters quickly, which substantially abates the mathematical computational overhead associated with MGPC. The feasibility and efficiency of this algorithm is demonstrated by comparison experiment results

关键词： Programmable control Adaptive control Predictive control Open loop systems Neural networks Multi-layer neural network Predictive models industrial control MIMO Pervasive computing

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