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Annual Conference of industrial Electronics Society

作者： Jili Tao Ning Wang National Laboratory of Industrial Control Technology Institute of Advanced Process Control University of Zhejiang Hangzhou China

The neuron control method based on RBF network and fuzzy scheme is proposed for a direct drive robot. In this intelligent control system, the RBF network is used to update the weights of neuron controller on-line according to the nearest neighbor-clustering algorithm. And a fuzzy scheme is designed to tune the neuron controller gain on-line. With the example of a direct drive robot manipulator with uncertainties and external coupling, the experiments of applying the proposed control method are made. The simulation results demonstrate that the proposed method can efficiently control the independent joint of the direct drive robot.

关键词： Neurons Radial basis function networks Fuzzy control Fuzzy neural networks Robot control Intelligent control Weight control control systems Manipulators Uncertainty

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On the design of Walcott-Zak sliding mode observer

On the design of Walcott-Zak sliding mode observer

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American control Conference (ACC)

作者： Ji Xiang Hongye Su Jian Chu National Laboratory of Industrial Control Technology Institute of Advanced Process Control University of Zhejiang Hangzhou China

ISBN: (纸本)0780390989

This paper explores the design of Walcott-Zak sliding mode observer (WZSMO) for a class of system satisfying some positive real conditions. A new sufficient and necessary existence condition only involving the original system parameters is proposed to simplify the design of WZSMO. The full freedom degree of the design of nonlinear injection of WZSMO is studied. The fact that the linear feedback can be designed independently is discovered. An optimizing design procedure of the nonlinear injection of SMO is proposed to sub-minimize the control cost of nonlinear injection. At last, a numerical example is presented to illustrate the proposed design procedure.

关键词： Feedback State estimation Laboratories industrial control process control Symmetric matrices Cost function Error correction Linear matrix inequalities Design optimization

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Neuron model-free control with fuzzy tuning parameters for hydraulic turbine generators

Neuron model-free control with fuzzy tuning parameters for h...

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Annual Conference of industrial Electronics Society

作者： Yaping Du Ning Wang National Laboratory of Industrial Control Technology Institute of Advanced Process Control University of Zhejiang Hangzhou China

Under the operating conditions of various guide vane opening, the characteristics of a hydraulic turbine generator unit show much difference in parameters. In order to control hydraulic turbine generators efficiently, the neuron model-free control method with fuzzy tuning parameters is proposed in this paper. In this control system, the neuron is designed to produce the control signal in model-free way, and the neuron controller parameters are tuned by using the fuzzy algorithm. With an example, the experiments are made. The simulation results show that the proposed neuron model-free controller has good performance, fast transient response and strong robustness.

关键词： Neurons Fuzzy control Hydraulic turbines Blades Character generation control system synthesis Signal design Algorithm design and analysis Fuzzy systems Transient response

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Robust sliding mode output feedback control design using ILMI approach

Robust sliding mode output feedback control design using ILM...

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American control Conference (ACC)

作者： Ji Xiang Hongye Su Jian Chu National Laboratory of Industrial Control Technology Institute of Advanced Process Control University of Zhejiang Hangzhou China

ISBN: (纸本)0780390989

In this paper, a new sufficient and necessary condition is proposed for the existence problem of sliding mode output feedback control (SMOFC). This condition only involves original system parameters. An iterative linear matrix inequality (ILMI) approach is developed on such new condition for the robust SMOFC design. The unit norm sliding matrix is proposed to limit the control cost. Finally, a numerical example illustrates the efficacy of the presented ILMI approach.

关键词： Robust control Output feedback control design Linear matrix inequalities Sliding mode control Robustness Laboratories industrial control process control Symmetric matrices

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Delay-dependent robust stabilization for uncertain linear system

Delay-dependent robust stabilization for uncertain linear sy...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Ren-Quan Lu Hong-Ye Su Jian Chu Institute of Intelligence Information and Control Technology Hangzhou Dianzi University China Institute of Advanced Process Control National Laboratory of Industrial Control Technology University of Zhejiang Hangzhou China

The problem of delay-dependent robust stabilization for a class of uncertain linear systems with time-varying delay is investigated. The parameter uncertainty under consideration is norm bounded and possibly time-varying. The time delay considered here is assumed to be size-bounded but otherwise arbitrarily fast time-varying. Based on a special Lyapunov-Krasovakii functional, the delay-dependent stability condition is formulated in terms of linear matrix inequalities. The delay-independent result can be derived as a particular case for certain values of the tuning parameters. A state-feedback control law is also given such that the resultant closed-loop system is stable for admissible uncertainties. Two numerical examples are given to demonstrate the efficiency of the obtained results.

关键词： Robustness Linear systems Time varying systems Delay systems Uncertain systems Delay effects Stability Linear matrix inequalities control systems Uncertainty

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Monitoring batch processes using multi-model discriminant partial least squares

Monitoring batch processes using multi-model discriminant pa...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Li-Ying Jiang Lei Xie Shu-Qing Wang Ning Wang National Laboratory of Industrial Control Technology Institute of Advanced Process Control University of Zhejiang Hangzhou China Shenyang Institute of Aeronautical Engineering Shenyang China

Multiway principal component analysis (MPCA) has been successfully used to monitor batch processes. But, there exist two problems when MPCA is used to monitor batch processes. One is that the process data must submit normal distribution. Another is that MPCA need complete batch data for online monitoring, so the values from the current until the end of its operation must been estimated or filled in. To overcome those problems, a novel monitoring method, multi-model discriminant partial least squares (multi-model DPLS), is presented. The proposed method is proved to be effective by the application of an industrial streptomycin fermentation process.

关键词： Monitoring Least squares methods Principal component analysis Page description languages industrial control Gaussian distribution Covariance matrix Aerospace engineering process control Chemical processes

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Weighted support vector machine for classification with uneven training class sizes

Weighted support vector machine for classification with unev...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Yi-Min Huang Shu-Xin Du Computer and Information Engineering Academy Zhejiang Gongshang University China National Key Laboratory of Industrial Control Technology Institute of Intelligent Systems and Decision Making

In the standard support vector machines for classification, training sets with uneven class sizes results in classification biases towards the class with the large training size. That is to say, the larger the training sample size for one class is, the smaller its corresponding classification error rate is, while the smaller the sample size, the larger the classification error rate. The main causes lie in that the penalty of misclassification for each training sample is considered equally. Weighted support vector machines for classification are proposed in this paper where penalty of misclassification for each training sample is different. By setting the equal penalty for the training samples belonging to same class, and setting the ratio of penalties for different classes to the inverse ratio of the training class sizes, the obtained weighted support vector machines compensate for the undesirable effects caused by the uneven training class size, and the classification accuracy for the class with small training size is improved. Experimental simulations on breast cancer diagnosis show the effectiveness of the proposed methods.

关键词： Support vector machines Support vector machine classification industrial training Error analysis Machine learning Object detection Fault diagnosis industrial control Intelligent systems Machine intelligence

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Weighted support vector machine for classification

Weighted support vector machine for classification

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IEEE International Conference on Systems, Man and Cybernetics

作者： Shu-Xin Du Sheng-Tan Chen National Key Laboratory of Industrial Control Technology Institute of Intelligent Systems and Decision Making University of Zhejiang Hangzhou China School of Mechanical Electronic Engineering Xídian University Xi'an China

In the standard support vector machines for classification, the use of training sets with uneven class sizes results in classification biases towards the class with the large training size. The main causes lie in that the penalty of misclassification for each training sample is considered equally. Weighted support vector machines for classification are proposed in this paper where penalty of misclassification for each training sample is different. By setting the equal penalty for the training samples belonging to same class, and setting the ratio of penalties for different classes to the inverse ratio of the training class sizes, the obtained weighted support vector machines compensate for the undesirable effects caused by the uneven training class size, and the classification accuracy for the class with small training size is improved. But this improvement is obtained at the cost of the possible decrease of classification accuracy for the class with large training size and the possible decrease of the total classification accuracy. Two weighted support vector machines, namely weighted C-SVM and V-SVM, corresponding to C-SVM and V-SVM are given respectively. Experimental simulations on breast cancer diagnosis show the effectiveness of the proposed methods.

关键词： Support vector machines Support vector machine classification Object detection Error analysis Fault diagnosis industrial control Intelligent systems Machine intelligence Decision making Electronic mail

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Fuzzy Neural Network Model of 4-CBA Concentration for industrial Purified Terephthalic Acid Oxidation process

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Chinese Journal of Chemical Engineering 2004年第2期12卷 234-239页

作者：刘瑞兰苏宏业牟盛静贾涛陈渭泉褚健 National Laboratory of Industrial Control Technology Institute of Advanced Process Control Zhejiang University Hangzhou 310027 China

A fuzzy neural network (FNN) model is developed to predict the 4-CBA concentration of the oxidation unit in purified terephthalic acid process. Several technologies are used to deal with the process data before ***,a set of preliminary input variables is selected according to prior knowledge and experience. Secondly,a method based on the maximum correlation coefficient is proposed to detect the dead time between the process variables and response variables. Finally, the fuzzy curve method is used to reduce the unimportant input *** simulation results based on industrial data show that the relative error range of the FNN model is narrower than that of the American Oil Company (AMOCO) model. Furthermore, the FNN model can predict the trend of the 4-CBA concentration more accurately.

关键词： purified terephthalic acid 4-carboxybenzaldchydc fuzzy neural network soft sensor input variables selection fuzzy curve dead time detection

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A Robust Statistical Batch process Monitoring Framework and Its Application

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Chinese Journal of Chemical Engineering 2004年第5期12卷 682-687页

作者：谢磊张建明王树青 National Key Laboratory of Industrial Control Technology Institute of Advanced Process Control Zhejiang University Hangzhou 310027 China

In order to reduce the variations of the product quality in batch processes, multivariate statistical process control methods according to multi-way principal component analysis (MPCA) or multi-way projection to latent structure (MPLS) were proposed for on-line batch process monitoring. However, they are based on the decomposition of relative covariance matrix and strongly affected by outlying observations. In this paper, in view of an efficient projection pursuit algorithm, a robust statistical batch process monitoring (RSBPM) framework,which is resistant to outliers, is proposed to reduce the high demand for modeling data. The construction of robust normal operating condition model and robust control limits are discussed in detail. It is evaluated on monitoring an industrial streptomycin fermentation process and compared with the conventional MPCA. The results show that the RSBPM framework is resistant to possible outliers and the robustness is confirmed.

关键词： robust statistical batch process monitoring robust principal componentanalysis streptomycin fermentation robust multi-way principal component analysis

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