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Mean square stability of discrete-time linear systems with random impulsive disturbances

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Science China(Information Sciences) 2023年第6期66卷 297-298页

作者： Jiamei LONG Yuqian GUO weihua gui School of Automation Central South University

Dear editor,In the real world,many dynamic processes may experience abrupt state [1] or parameter changes. Mathematically,these abrupt changes of state can be modeled as ideal impulsive effects. And also,the moments a... 详细信息

关键词： Linear systems

Parameter Optimization of a Short Open Optical Path's Oxygen Concentration Detection System Based on WMS

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Chinese Journal of Electronics 2017年第4期26卷 797-802页

作者： ZHU Gaofeng ZHU Hongqiu YANG Chunhua gui weihua School of Information Science and Engineering Central South University Department of Information Hunan University of HumanitiesScience and Technology

In Wavelength modulation spectroscopy(WMS) system with a short open optical path to detect the oxygen concentration, the system parameter optimization is the preconditions for the accurate inversion of oxygen concentration. Absorption spectroscopy parameters of oxygen were obtained from the HIRTAN database, and the optimal center wavelength and its wavelength offset were confirmed. The laser’s working temperature and direct current were determined according to the absorption peak position of photoelectric detection. Modulation parameters and time constant of lock-in amplifier were determined by analyzing intensity and standard deviation of the second harmonic signal. This method has been applied to detect the oxygen concentration in a glass vial, and the results indicate that the linear correlation coefficient of concentration and the second harmonic intensity is 0.9950. The resolution and stability of the detection system have been improved and it can meet the requirements of industrial field.

关键词： Parameter optimization Oxygen con centration Wavelength modulation spectroscopy(WMS) Short open optical path Second harmonic signals

Asynchronous Fuzzy Cognitive Networks Modeling and Control for Goethite Iron Precipitation Process

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Journal of Systems Science & Complexity 2020年第5期33卷 1422-1445页

作者： CHEN Ning PENG Junjie gui weihua ZHOU Jiaqi DAI Jiayang School of Automation Central South UniversityChangsha 410083China School of ElectricalEngineering Guangxi UniversityNanning 530004China

Goethite iron precipitation process is a key step in direct leaching process of zinc,whose aim is to remove ferrous ions from zinc sulphate *** process consists of several cascade reactors,and each of them contains complex chemical reactions featured by strong nonlinearity and large time ***,it is hard to build up an accurate mathematical model to describe the dynamic changes in the *** this paper,by studying the mechanism of these reactions and combining historical data and expert experience,the modeling method called asynchronous fuzzy cognitive networks(AFCN)is proposed to solve the various time delay ***,the corresponding AFCN model for goethite iron precipitation process is *** control the process according to fuzzy rules,the nonlinear Hebbian learning algorithm(NHL)terminal constraints is firstly adopted for weights *** the model parameters of equilibrium intervals corresponding to different operating conditions can be ***,the matrix meeting the expected value and the weight value of steady states is stored into fuzzy rules as prior *** simulation shows that the AFCN model for goethite iron precipitation process could precisely describe the dynamic changes in the system,and verifies the superiority of control method based on fuzzy rules.

关键词： Asynchronous fuzzy cognitive networks fuzzy rules database Goethite iron precipitation process Hebbian learning

Integral Event-Triggered Attack-Resilient Control of Aircraft-on-Ground Synergistic Turning System With Uncertain Tire Cornering Stiffness

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IEEE/CAA Journal of Automatica Sinica 2023年第5期10卷 1276-1287页

作者： Chenglong Du Fanbiao Li Yang Shi Chunhua Yang weihua gui the School of Automation Central South UniversityChangsha 410083China the Department of Mechanical Engineering University of VictoriaVictoria BC V8W 3P6Canada

This article proposes an integral-based event-triggered attack-resilient control method for the aircraft-on-ground(AoG) synergistic turning system with uncertain tire cornering stiffness under stochastic deception attacks. First, a novel AoG synergistic turning model is established with synergistic reverse steering of the front and main wheels to decrease the steering angle of the AoG fuselage, thus reducing the steady-state error when it follows a path with some large curvature. Considering that the tire cornering stiffness of the front and main wheels vary during steering, a dynamical observer is designed to adaptively identify them and estimate the system state at the same ***, an integral-based event-triggered mechanism(I-ETM) is synthesized to reduce the transmission frequency at the observerto-controller end, where stochastic deception attacks may occur at any time with a stochastic probability. Moreover, an attackresilient controller is designed to guarantee that the closed-loop system is robust L2-stable under stochastic attacks and external disturbances. A co-design method is provided to get feasible solutions for the observer, controller, and I-ETM simultaneously. An optimization program is further presented to make a tradeoff between the robustness of the control scheme and the saving of communication resources. Finally, the low-and high-probability stochastic deception attacks are considered in the simulations. The results have illustrated that the AoG synergistic turning system with the proposed control method follows a path with some large curvature well under stochastic deception attacks. Furthermore,compared with the static event-triggered mechanisms, the proposed I-ETM has demonstrated its superiority in saving communication resources.

关键词： Adaptive observer aircraft-on-ground(AoG)synergistic turning attack-resilient controller integral-based event-triggered mechanism L_2-stability

Smart manufacturing of nonferrous metallurgical processes:Review and perspectives

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International Journal of Minerals,Metallurgy and Materials 2022年第4期29卷 611-625页

作者： Bei Sun Juntao Dai Keke Huang Chunhua Yang weihua gui School of Automation Central South UniversityChangsha 410083China Peng Cheng Laboratory Shenzhen 518000China

The nonferrous metallurgical(NFM)industry is a cornerstone industry for a nation’s *** the development of artificial technologies and high requirements on environment protection,product quality,and production efficiency,the importance of applying smart manufacturing technologies to comprehensively percept production states and intelligently optimize process operations is becoming widely recognized by the *** a brief summary of the smart and optimal manufacturing of the NFM industry,this paper first reviews the research progress on some key facets of the operational optimization of NFM processes,including production and management,blending optimization,modeling,process monitoring,optimization,and ***,it illustrates the perspectives of smart and optimal manufacturing of the NFM ***,it discusses the major research directions and challenges of smart and optimal manufacturing for the NFM *** paper will lay a foundation for the realization of smart and optimal manufacturing in nonferrous metallurgy in the future.

关键词： nonferrous metallurgical industry smart and optimal manufacturing online perception intelligent control operational optimiza-tion automation of knowledge-based work

Dust Distribution Study at the Blast Furnace Top Based on k-Sε-u_(p)Model

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IEEE/CAA Journal of Automatica Sinica 2021年第1期8卷 121-135页

作者： Zhipeng Chen Zhaohui Jiang Chunjie Yang weihua gui Youxian Sun the School of Automation Central South UniversityChangsha 410083China the School of Control Science and Engineering Zhejiang UniversityHangzhou 310027China

The dust distribution law acting at the top of a blast fumace(BF)is of great significance for understanding gas flow distribution and mitigating the negative influence of dust particles on the accuracy and service life of detection *** harsh environment inside a BF makes it difficult to describe the dust *** paper adresses this problem by proposing a dust distribution k-Sε-u_(p)model based on interphase(gas-powder)*** proposed model is coupled with a k-Sεmodel(which describes gas flow movement)and a u_(p)model(which depicts dust movement).First,the kinetic energy equation and turbulent dissipation rate equation in the k-Sεmodel are established based on the modeling theory and single Green-function two scale direct interaction approximation(SGF-TSDIA)***,a dust particle mnovement u_(p)model is built based on a force analysis of the dust and Newton's laws of ***,a coupling factor that descibes the interphase interaction is proposed,and the k-Sε-u_(p)model,with clear physical *** mathematical logic,and adequate generality,is *** results and o-site verification show that the k-Sε-u_(p)model not only has high precision,but also reveals the aggregate distribution features of the dust,which are helpful in optimizing the installation position of the detection equipment and imnproving its accuracy and service life.

关键词： Blast furnace(BF) dust movement interphase interaction modeling theory turbulent flow two-scale direct interaction approximation(TSDIA)

A Deep Residual PLS for Data-Driven Quality Prediction Modeling in Industrial Process

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IEEE/CAA Journal of Automatica Sinica 2024年第8期11卷 1777-1785页

作者： Xiaofeng Yuan Weiwei Xu Yalin Wang Chunhua Yang weihua gui School of Automation Central South UniversityChangsha 410083China She is now with Guangzhou Electromechanical Technician College Guangzhou 510000China

Partial least squares(PLS)model is the most typical data-driven method for quality-related industrial tasks like soft ***,only linear relations are captured between the input and output data in the *** is difficult to obtain the remaining nonlinear information in the residual subspaces,which may deteriorate the prediction performance in complex industrial *** fully utilize data information in PLS residual subspaces,a deep residual PLS(DRPLS)framework is proposed for quality prediction in this *** by deep learning,DRPLS is designed by stacking a number of PLSs successively,in which the input residuals of the previous PLS are used as the layer *** enhance representation,nonlinear function is applied to the input residuals before using them for stacking highlevel *** each PLS,the output parts are just the output residuals from its previous ***,the output prediction is obtained by adding the results of each *** effectiveness of the proposed DRPLS is validated on an industrial hydrocracking process.

关键词： Deep residual partial least squares(DRPLS) nonlinear function quality prediction soft sensor

Visual Perception-Based Fault Diagnosis in Froth Flotation Using Statistical Approaches

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Tsinghua Science and Technology 2021年第2期26卷 172-184页

作者： Jin Zhang Zhaohui Tang Yongfang Xie Mingxi Ai weihua gui School of Automation Central South UniversityChangsha 411083China

Froth flotation is an important mineral concentration *** conditions in flotation processes may cause the huge waste of mineral resources and reagents,and consequently,may lead to deterioration in terms of benefits of flotation *** this paper,we propose a computer vision-aided fault detection and diagnosis approach for froth ***,a joint Gabor texture feature based on the Copula model is designed to describe froth images;a rejection sampling technique is developed to generate training sets from the quality distribution of real flotation products,and then an isolation forest-based fault detector is learned;and a fault diagnosis model based on spline regression is developed for root cause *** experiments conducted on the historical industry data show that the proposed strategy has better performance than the alternative ***,the entire framework has been tested on a lead-zinc flotation plant in *** results have demonstrated the effectiveness of the proposed method.

关键词： fault diagnosis Gabor copula model isolation forest rejection sampling

Label propagation dictionary learning based process monitoring method for industrial process with between-mode similarity

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Science China(Information Sciences) 2022年第1期65卷 39-55页

作者： Keke HUANG Shijun TAO Yishun LIU Chunhua YANG weihua gui School of Automation Central South University

With the development of the industrial cyber-physical systems, a small amount of labeled data and a large amount of unlabeled data are collected from the industrial process. Due to the variation of internal operation conditions and external environment, there is a between-mode similarity between data samples. The scarcity of labeled data and the existence of similarity make it challenging to extract data characteristics. In addition, it creates new challenges to process monitoring. To solve these problems, this study proposes a label propagation dictionary learning method. We first establish the connection between atoms and corresponding profiles and realize the propagation of their labels through graph Laplacian regularization. Then, considering the similarity of samples in the same class, the low-rank constraint is added to sparse coding to strengthen the mutual propagation of labels. Finally, an optimization method is designed to obtain the dictionary and classifier simultaneously. When new data samples arrive, we conduct process monitoring and condition prediction based on the learned dictionary and classifier. Experiments show that the proposed method can achieve satisfactory monitoring performance when compared to several state-of-the-art methods, indicating the superiority of the proposed method.

关键词： process monitoring label propagation dictionary learning between-mode similarity graph Laplacian regularization

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