Weihua Gui

Central South University, Ch’ang-sha-shih, Hunan, China

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Publications (198)133.62 Total impact

  • Control Engineering Practice 01/2016; 46:10-25. DOI:10.1016/j.conengprac.2015.09.006 · 1.81 Impact Factor
  • Hui Wang · Mei Su · Yao Sun · Jian Yang · Guanguan Zhang · Weihua Gui · Jianghua Feng ·
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    ABSTRACT: Due to the merits of sinusoidal input currents and excellent conversion efficiency, buck-type active third-harmonic injection converters have received increasing attention in recent years. However, three major problems of these converters still exist in theory and practice. First, the existence of the dc-link capacitor, utilized by early researchers, causes inrush transient and distorts the input currents. Second, in some specific situations, such as wind energy conversion system and flexible ac transmission system, these converters' capabilities of generating input reactive power need sufficient improvements. Third, proper selection of the third-harmonic injection inductor is the key challenge to implement these converters, since the inductor affects not only the current ripple but also the tracking performance of the third-harmonic current. To solve these problems, this paper studies the two-stage matrix converter with third harmonics injection and demonstrates that the dc-link capacitor can be removed by bidirectional implementation and proper control of the rectifier. Then, an algorithm enhancing the input reactive power capability is developed. Thus, sinusoidal input currents are achieved and the reactive power control range is extended significantly. Moreover, the design criteria of the third-harmonic injection inductor are discussed. Finally, the proposed method is verified by simulation and experimental results.
    IEEE Transactions on Power Electronics 01/2016; 31(1):533-547. DOI:10.1109/TPEL.2015.2413452 · 6.01 Impact Factor
  • Bei Sun · Weihua Gui · Yalin Wang · Chunhua Yang · Mingfang He ·
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    ABSTRACT: This paper presents a two-layer control scheme to address the difficulties in the modeling and control of solution purification process. Two concepts are extracted from the characteristics of solution purification process: additive utilization efficiency (AUE) and impurity removal ratio (IRR). The idea of gradient optimization of solution purification process, which transforms the economical optimization problem of solution purification process into finding an optimal decline gradient of the impurity ion concentration along the reactors, is proposed. A robust adaptive controller is designed to track the optimized impurity ion concentration in the presence of process uncertainties, disturbance and saturation. Oxidation reduction potential (ORP), which is a significant parameter of solution purification process, is also used in the scheme. The ability of the gradient optimization scheme is illustrated with a simulated case study of a cobalt removal process.
    Control Engineering Practice 11/2015; 44:89-103. DOI:10.1016/j.conengprac.2015.07.008 · 1.81 Impact Factor
  • Bin Zhang · Chunhua Yang · Hongqiu Zhu · Yonggang Li · Weihua Gui ·
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    ABSTRACT: The copper removal process purifies copper from leaching solutions with zinc powder in reactors. Due to the complex reaction mechanism and unavailability of online measurements, zinc powder is usually added inexactly, which easily leads to unstable production. This paper proposes an online evaluation method based on oxidation-reduction potential (ORP) for the control of the copper removal process. A kinetic model is designed to translate the production requirements to evaluation indexes of ORP, and the process is then graded by evaluating the fuzzified ORP and its trends according to these indexes. By analyzing these evaluation grades, the process condition is divided into several classes, and each condition class corresponds to a control method set. The industrial experiments show that the copper removal performance is improved by using the proposed evaluation and control strategy.
  • Source
    Xiaoli Wang · Yalin Wang · Chunhua Yang · Degang Xu · Weihua Gui ·
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    ABSTRACT: An industrial grinding-classification process of diasporic bauxite is modeled based on the integration of phenomenological and statistical learning methods. The breakage characteristics of the ore and running status of the whole process are first investigated by laboratory testing and process sampling, respectively. Based on the population balance model (PBM) framework, the breakage distribution function is estimated from laboratory test data. The breakage rates are back-calculated directly from the industrial data, where a nonlinear breakage rate function is proposed for coarse particles. They are then correlated to the operating variables (including the water flow rate and feed flow rate), ball characteristics and material properties using the least squares support vector machine (LSSVM) method so that the model is suitable to various grinding conditions. Material transportation through the mill was treated as two equal smaller fully mixed reactors followed by a large one. The particle size distribution (PSD) of the mill product is then predicted by sequentially solving the reactors in series, considering the nonlinear breakage kinetics. A spiral classifier model is obtained with the Rosin–Rammer curve, where the bypass, real classification effect and operating conditions are included. The simulation results of the whole process by using the sequential module approach (SMA) demonstrate reasonable agreement between the predicted and measured industrial process data. The models are finally applied to the process for the prediction of particle size indices and to provide valuable information for the operation and further optimization of the process.
    Powder Technology 07/2015; 279. DOI:10.1016/j.powtec.2015.03.031 · 2.35 Impact Factor
  • Source
    Xiaojun Zhou · Peng Shi · Cheng-Chew Lim · Chunhua Yang · Weihua Gui ·
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    ABSTRACT: To investigate the energy consumption involved in an event based control scheme, the problem of event based guaranteed cost consensus for distributed multi-agent systems with general linear time invariant dynamics is considered in this paper. A delay system method is used to transform the multi-agent systems into a special delay system based on a sampled-data event triggering mechanism, which only requires supervision of system states at discrete instants. Sufficient conditions to achieve the consensus with guaranteed cost are presented and expressed as a continuous constrained optimization problem with a linear objective function, linear and bilinear matrix inequalities constraints, involving the co-design of the controller gain matrix and event triggering parameters. An illustrative example is given to show the effectiveness of the proposed approach.
    Journal of the Franklin Institute 03/2015; 352(9). DOI:10.1016/j.jfranklin.2015.02.012 · 2.40 Impact Factor
  • Ling Shen · Jianjun He · Chunhua Yang · Weihua Gui · Honglei Xu ·

    IEEE Transactions on Control Systems Technology 01/2015; DOI:10.1109/TCST.2015.2417495 · 2.47 Impact Factor
  • Bei Sun · Chunhua Yang · Weihua Gui ·

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    ABSTRACT: Iron precipitation by goethite plays an important role in zinc hydrometallurgy. The ferrous ion concentration, which is a key index for assessing the iron removal rate and process control results, cannot be measured on-line. In this study, an integrated predictive model of the ferrous ion concentration is established by integrating the mechanism model and error compensation model, which is based on data identification. The mechanism model is proposed based on an analysis of the process reaction and considering the reaction unit as a continuous stirred tank reactor model. For unknown parameters in the mechanism model, a double-particle swarm optimization algorithm based on information exchange and dynamic adjustment of the feasible region is developed for optimal selection. To improve the adaptive capability of the integrated model, we propose a model-updating strategy and parameter calibration method based on a sensitivity analysis to accomplish on-line adaptive updating of the predictive model. The simulation results demonstrate that the proposed model can effectively track the variation tendency of the ferrous ion concentration and successfully improve the adaptability of the integrated model.
    Hydrometallurgy 11/2014; 151. DOI:10.1016/j.hydromet.2014.11.004 · 1.93 Impact Factor
  • Zhifeng Qiu · Ning Gui · Chunhua Yang · Geert Deconinck · Weihua Gui ·
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    ABSTRACT: In the context of liberalized markets, market outcomes generally result from the strategic interactions of all market players. Generation company (Genco), as the distributed players, build their subjective demand evaluations (SDFs) about market for optimal bidding purpose. Due to the differences in terms of data availability and modeling techniques, subjective demand models held by various Gencos are heterogeneous and normally deviate from the real market model as well. The picture of a real electricity market game in Genco’s eye is ‘playing is believing’. Therefore, a question naturally comes to the table: how those SDFs with the heterogeneous manner impact individual player’s decision and game results. To answer this question, this paper relaxes a conventional assumption, commonly used in the classical oligopolistic equilibrium model, that one correct and uniform demand knowledge is shared by all Gencos. The results suggest that the system equilibriums would be influenced by the Gencos’ knowledge about market demand. The economic value of demand information is assessed regarding the system performances.
    International Journal of Electrical Power & Energy Systems 09/2014; 60:182–189. DOI:10.1016/j.ijepes.2014.02.018 · 3.43 Impact Factor
  • Ning Chen · Guisheng Zhai · Yuqian Guo · Weihua Gui · Xiaoyu Shen ·
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    ABSTRACT: This paper studies the analysis of parametric stability and decentralized state feedback control of a kind of quantized interconnected systems. The output of each controller is quantized logarithmically before it is input to the subsystem, and the quantized density would affect the stability of the systems. First, a decentralized state feedback controller is designed for interconnected systems without quantization and the corresponding stable region is obtained. Second, for a given controller, the lower bound of the quantization density is evaluated from parameters of local controllers. Finally, the proposed method is applied to coupled inverted pendulums systems which can be viewed as quantized interconnected systems. The simulation results show that by using the proposed quantized controllers, the interconnected inverted pendulum systems are parametrically stabilized.
    Asian Journal of Control 09/2014; 17(3). DOI:10.1002/asjc.955 · 1.56 Impact Factor
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    ABSTRACT: In this study, the guaranteed cost control of discrete time uncertain system with both state and input delays is considered. Sufficient conditions for the existence of a memoryless state feedback guaranteed cost control law are given in the bilinear matrix inequality form, which needs much less auxiliary matrix variables and storage space. Furthermore, the design of guaranteed cost controller is reformulated as an optimization problem with a linear objective function, bilinear, and linear matrix inequalities constraints. A nonlinear semi-definite optimization solver—PENLAB is used as a solution technique. A numerical example is given to demonstrate the effectiveness of the proposed method. Copyright © 2014 John Wiley & Sons, Ltd.
    Optimal Control Applications and Methods 08/2014; DOI:10.1002/oca.2138 · 0.90 Impact Factor
  • Jianyong Zhu · Weihua Gui · Chunhua Yang · Honglei Xu · Xiaoli Wang ·
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    ABSTRACT: As an effective measurement indicator of bubble stability, bubble size structure is believed to be closely related to flotation performance in copper roughing flotation. Moreover, reagent dosage has a very important influence on bubble size structure. In this paper, a novel reagent dosage predictive control method based on probability density function (PDF) of bubble size is proposed to implement the indices of roughing circuit. Firstly, the froth images captured in the copper roughing are segmented by using a two-pass watershed algorithm. In order to characterize bubble size structure with non-Gaussian feature, an entropy based B-spline estimator is hence investigated to depict the PDF of the bubble size. Since the weights of B-spline are interrelated and related to the reagent dosage, a multi-output least square support vector machine (MLS-SVM) is applied to depict a dynamic relationship between the weights and the reagent dosage. Finally, an entropy based optimization algorithm is proposed to determine reagent dosage in order to implement tracking control for the PDF of the output bubble size. Experimental results can show the effectiveness of the proposed method.
    Control Engineering Practice 08/2014; 29:1–12. DOI:10.1016/j.conengprac.2014.02.021 · 1.81 Impact Factor
  • Ning Chen · Xiaojun Hu · Weihua Gui · Jiachi Zou ·
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    ABSTRACT: In order to obtain the accurate estimation of SOC (State of Charge) and predicted lithium battery SOH (State of Healthy), this article is based upon the internal resistance of the battery model. By using UKF method, the estimation of SOC and SOH can be carried out in the nonlinear conditions. The UKF algorithm considers the internal resistance of the model parameters and SOC as the state parameters. Depending on UKF, the SOC will be estimated and the resistance will be constantly adjusted to compensate for model inaccuracies. Due to the internal resistance has the relation with the state, thus the SOH indirectly would be estimated. The final simulations and the result of the experiments show that unscented Kalman filter can make the accuracy of SOC estimation within 4% while achieving an accurate prediction of the SOH.
    2014 26th Chinese Control And Decision Conference (CCDC); 05/2014
  • Jianyong Zhu · Weihua Gui · Chunhua Yang · Jinping Liu · Yongbo Tang ·
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    ABSTRACT: As an effective measurement of bubble stability, bubble size structure is believed to be closely related to flotation performance. Reagent dosage has a very important influence on bubble size. A novel probability density function (PDF) of bubble size based reagent dosage control method is proposed in the paper. First, froth images acquired by camera with big and tiny blobs are segmented by use of an improved two-pass watershed algorithm. With non-Gaussian features, the PDF of the bubble size is hence approximated by the estimator based on B-spline such that the PDF of the bubble size is characterized by a weight vector of the B-spline. Finally, a PDF-based reagent dosage control algorithm is proposed to make the output PDF track the given PDF according to the linear matrix inequality technique, which is established to solve its stability condition by Lyapunov stability analysis. Experimental results show the effectiveness of the proposed method.
    Asian Journal of Control 05/2014; 16(3). DOI:10.1002/asjc.847 · 1.56 Impact Factor
  • Zhikun Hu · Zhiwen Chen · Weihua Gui · Bin Jiang ·
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    ABSTRACT: In this paper, an adaptive fault detection scheme based on a recursive principal component analysis (PCA) is proposed to deal with the problem of false alarm due to normal process changes in real process. Our further study is also dedicated to develop a fault isolation approach based on Generalized Likelihood Ratio (GLR) test and Singular Value Decomposition (SVD) which is one of general techniques of PCA, on which the off-set and scaling fault can be easily isolated with explicit off-set fault direction and scaling fault classification. The identification of off-set and scaling fault is also applied. The complete scheme of PCA-based fault diagnosis procedure is proposed. The proposed scheme is first applied to Imperial Smelting Process, and the results show that the proposed strategies can be able to mitigate false alarms and isolate faults efficiently.
    ISA Transactions 01/2014; 53(5). DOI:10.1016/j.isatra.2013.12.018 · 2.98 Impact Factor
  • Source
    Ning Chen · Yutian Liu · Bo Liu · Weihua Gui ·
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    ABSTRACT: The paper considers the parametric absolute stabilization for interconnected Lurie time-delay systems with polytopic uncertainty. The concept of parametric absolute stabilization characterizes both the existence and the stability of equilibrium in the case of uncertain parameters and reference input shift. First, the existing conditions of parametric stability and the stable region are studied by the change of the uncertain parameters and reference input based on decentralized state feedback. Then, a delay-dependent absolute stability condition in parametric stabilization region for interconnected Lurie time-delay systems with polytopic uncertainties is obtained through a linear matrix inequality method. Finally, an example is given to illustrate the effectiveness of the proposed method.
    Asian Journal of Control 01/2014; 16(1). DOI:10.1002/asjc.661 · 1.56 Impact Factor
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    ABSTRACT: In flotation processes, concentrate grade is the key production index but is difficult to be measured online. The mechanism models reflect the basic tendency of concentrate grade changes but cannot provide adequate prediction precision. The data-driven models based on froth image features provide accurate prediction within well-sampled space but rely heavily on sample data with less generalization capability. So, a hybrid intelligent model combining the two kinds of model is proposed in this paper. Since the information of image features is enormous, and the relationship between image features and concentrate grade is nonlinear, a B-spline partial least squares (BS-PLS) method is adopted to construct the data-driven model for concentrate grade prediction. In order to gain better generalization capability and prediction accuracy, information entropy is introduced to integrate the mechanism model and the BS-PLS model together and modify the model output online through an output deviation compensation strategy. Moreover, a slide window scheme is employed to update the hybrid model in order to improve its adaptability. The industrial practical data testing results show that the performance of the hybrid model is better than either of the two single models and it satisfies the accuracy and stability requirements in industrial applications.
    Abstract and Applied Analysis 01/2014; 2014(23):1-17. DOI:10.1155/2014/401380 · 1.27 Impact Factor
  • Bin Zhang · Chunhua Yang · Hongqiu Zhu · Yonggang Li · Weihua Gui ·
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    ABSTRACT: In zinc hydrometallurgy, an advanced copper removal process purifies zinc sulfate solution through a series of chemical reactions with recycled underflow by using zinc powder in zinc hydrometallurgy. This paper focuses on the kinetic modeling of the competitive-consecutive reaction system in the copper removal process, and proposes an adaptive parameter optimal selection strategy for different industrial conditions. In the system model, copper cementation, one of the removal reactions, is described by a surface controlled pseudo-first-order rate equation; cuprous oxide precipitation, the other removal reaction, is described by a shrinking core model of a noncatalytic fluid–solid reaction. Because there are several kinetic parameters in the system model, parameter estimation plays an essential role. Because of the complexity and variation in the practical removal process, the kinetic parameters are usually sensitive to alterations in the process conditions. This work solves the parameter estimation problem using an optimal selection strategy. In the strategy, the industrial conditions are classified adaptively according to the system model performance, then the kinetic parameters are selected optimally by evolutionary and particle swarm optimization algorithms for different industrial conditions. Three different representative industrial data sets are used to test the effectiveness and flexibility of the proposed modeling and parameter optimal selection approach in various situations. Finally, the kinetic model is applied to the soft measurement of the practical copper removal process with underflow, and the results demonstrate that the model effectively captures the trends of the removal reactions.
    Industrial & Engineering Chemistry Research 11/2013; 52(48):17074–17086. DOI:10.1021/ie401619h · 2.59 Impact Factor
  • Binfang Cao · Yongfang Xie · Weihua Gui · Lijun Wei · Chunhua Yang ·
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    ABSTRACT: Concentrate grade of bauxite flotation is an important technology indicator, which has a direct effect on aluminum quality. Considering the unity, locality and inaccuracy of existing prediction methods of concentrate grade based on machine vision, a distributed machine vision system of bauxite flotation process is built in this paper, from which an integrated prediction model of concentrate grade is presented. At first, we use experimental methods to analyse image data from different flotation stages, as well as comment on the relationship between its global trends and local trends. Then taking advantage of the multiple kernels least squares support vector machine and wavelet extreme learning machine, models for prediction of concentrate grade and its residual compensation are established respectively to predict the concentrate grade through integration. Finally, validation and industrial applications show that the integrated prediction model based on distributed machine vision has a good generalization capability, which can achieve a good prediction accuracy of concentrate grade, with a relative error of less than 6%, thus laying a foundation for optimal control based on mineral grade in flotation process. (C) 2013 Published by Elsevier Ltd.
    Minerals Engineering 11/2013; 53:31-38. DOI:10.1016/j.mineng.2013.07.003 · 1.60 Impact Factor

Publication Stats

657 Citations
133.62 Total Impact Points


  • 2002-2015
    • Central South University
      • School of Information Science and Engineering
      Ch’ang-sha-shih, Hunan, China
  • 2007
    • Shaoguan University
      Chao-kuan, Guangdong, China
  • 2006
    • CSR Zhuzhou Electric Locomotive Research Institute
      Chu-chou-shih, Hunan, China
    • South Central College
      Central, Louisiana, United States
  • 1994-2000
    • Central South University of Forestry and Technology
      Ch’ang-sha-shih, Hunan, China