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arXiv 2024年

作者： Zhang, Renyongkang Guo, Ge Zhou, Zeng-Di College of Information Science and Engineering Northeastern University Shenyang110819 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao066004 China

This paper proposes a distributed optimization algorithm with a convergence time that can be assigned in advance according to task requirements. To this end, a sliding manifold is introduced to achieve the sum of local gradients approaching zero, based on which a distributed protocol is derived to reach a consensus minimizing the global cost. A novel approach for convergence analysis is derived in a unified settling time framework, resulting in an algorithm that can precisely converge to the optimal solution at the prescribed time. The method is interesting as it simply requires the primal states to be shared over the network, which implies less communication requirements. The result is extended to scenarios with time-varying objective function, by introducing local gradients prediction and non-smooth consensus terms. Numerical simulations are provided to corroborate the effectiveness of the proposed algorithms. © 2024, CC BY.

关键词： Consensus algorithm

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Semi-Supervised process Data Regression and Application Based on Latent Factor Analysis Model

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IEEE Transactions on Instrumentation and Measurement 2023年 72卷 1页

作者： Zheng, Junhua Liu, Yangxuan Liu, Yi Hou, Beiping Yao, Yuan Zhou, Le Zhejiang University of Science and Technology School of Automation and Electrical Engineering Hangzhou310023 China Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou310023 China National Tsing Hua University Department of Chemical Engineering Hsinchu30013 Taiwan

This article investigates the problem of modeling regression with labeled and unlabeled data samples commonly found in industrial processes. By incorporating additional information on unlabeled data samples, a new semi-supervised latent factor analysis model is developed. Compared with the purely supervised regression models which can only use the information of labeled dataset, the semi-supervised model can efficiently extract useful information for improvement of the regression performance. Furthermore, the proposed basic semi-supervised model has been extended to a mixture form, which is capable of describing data from more complex processes. For predictive modeling for key/mass variables, two soft sensors are constructed based on the semi-supervised models. Then, three case studies are used to evaluate the performance of the proposed soft sensing methods. © 2023 IEEE.

关键词： Regression analysis

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Study on series voltage equalizing method of IGBT in high voltage circuits 3

Study on series voltage equalizing method of IGBT in high vo...

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3rd International Conference on Intelligent control, Measurement and Signal processing and Intelligent Oil Field, ICMSP 2021

作者： Yang, Jiahui Cao, Ke Li, Hong School of Electronic Engineering Xi'An Shiyou University Automation Instrument and Process Control Laboratory Xi an China Shannxi Gaoke Power Electronics Ltd Xi an China

ISBN: (纸本)9781665437158

In this paper, a gate $RC-\mathrm{D}$ voltage equalizer circuit is studied. The purpose of this circuit is to solve the problem that the emitter voltage distribution of each component set is uneven when IGBT runs in series. The working principle of gate $RC-\mathrm{D}$ voltage equalizer is described, and the functions of all components in the voltage equalizer are analyzed under different switching states of IGBT. Finally, a series voltage equalizer circuit model of IGBT is built on PSPICE platform for simulation. By comparing the simulation results of voltage equalizing circuit with or without voltage equalizing circuit, a good voltage equalizing effect is obtained after simulating voltage imbalance under 4000V DC voltage. It provides a guarantee condition for the stable operation of IGBT in series, thus verifying the feasibility of the scheme. © 2021 IEEE.

关键词： Insulated gate bipolar transistors (IGBT)

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Circular Formation Flight control for Unmanned Aerial Vehicles With Directed Network and External Disturbance

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IEEE/CAA Journal of Automatica Sinica 2020年第2期7卷 505-516页

作者： Yangyang Chen Rui Yu Ya Zhang Chenglin Liu School of Automation and Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of EducationSoutheast UniversityNanjing 210096China School of Automation Southeast UniversityNanjing 210096China Key Laboratory of Advanced Process Control for Light Industry Ministry of EducationInstitute of AutomationJiangnan UniversityWuxi 214122China

This paper proposes a new distributed formation flight protocol for unmanned aerial vehicles(UAVs)to perform coordinated circular tracking around a set of circles on a target *** from the previous results limited in bidirectional networks and disturbance-free motions,this paper handles the circular formation flight control problem with both directed network and spatiotemporal disturbance with the knowledge of its upper *** from the design of a common Lyapunov fiunction for bidirectional cases,we separately design the control for the circular tracking subsystem and the formation keeping subsystem with the circular tracking error as *** the whole control system is regarded as a cascade connection of these two subsystems,which is proved to be stable by input-tostate stability(ISS)*** the purpose of encountering the external disturbance,the backstepping technology is introduced to design the control inputs of each UAV pointing to North and Down along the special sphere(say,the circular tracking control algorithm)with the help of the switching ***,the distributed linear consensus protocol integrated with anther switching anti-interference item is developed to construct the control input of each UAV pointing to east along the special sphere(say,the formation keeping control law)for formation *** validity of the proposed control law is proved both in the rigorous theory and through numerical simulations.

关键词： Directed network external disturbance flight control unmanned aerial vehicles(UAVs)

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Privacy Preservation by Intermittent Transmission in Cooperative LQG control Systems

Privacy Preservation by Intermittent Transmission in Coopera...

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Asian control Conference

作者： Wenhao Lin Yuqing Ni Wen Yang Chao Yang Department of Automation Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai China Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) School of Internet of Things Engineering Jiangnan University Wuxi China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

In this paper, we study a cooperative linear quadratic Gaussian (LQG) control system with a single user and a server. In this system, the user runs a process and employs the server to meet the needs of computation. However, the user regards its state trajectories as privacy. Therefore, we propose a privacy scheme, in which the user sends data to the server intermittently. By this scheme, the server's received information of the user is reduced, and consequently the user's privacy is preserved. In this paper, we consider a periodic transmission scheme. We analyze the performance of privacy preservation and LQG control of different transmission periods. Under the given threshold of the control performance loss, a trade-off optimization problem is proposed. Finally, we give the solution to the optimization problem.

关键词： Measurement Privacy Data privacy Networked control systems Trajectory Servers Optimization

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Sparse Causality Analysis Approach with Time-varying Parameters for Root Cause Localization of Nonstationary process 4

Sparse Causality Analysis Approach with Time-varying Paramet...

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4th International Conference on Industrial Artificial Intelligence, IAI 2022

作者： Song, Pengyu Zhao, Chunhui Huang, Biao Ding, Jinliang College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China University of Alberta Department of Chemical and Materials Engineering EdmontonABT6G 2G6 Canada Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China

ISBN: (数字)9781665451208

ISBN: (纸本)9781665451208

Root cause diagnosis (RCD) is an important technique for maintaining the safe operation of industrial processes. Traditional RCD methods usually require stationarity assumptions. However, the process inevitably shows nonstationarity due to factors such as switching of operating conditions. Although there have been some previous studies trying to overcome the challenge of nonstationarity, these methods fail to guarantee the significance of the extracted causalities and lead to redundant relationships. To address the above issues, a sparse causal analysis model with time-varying parameters is extracted in this study. First, we propose an end-to-end information fusion and prediction task to characterize predictive relationships between variables and avoid repeated modeling. Second, we design time-varying parameters for the information fusion mechanism to cope with nonstationarity and automatically identify significant causality through sparse parameter updates. We design an update strategy that constrains the gradient information to guarantee sparsity. Finally, a causal metric is constructed for the time-varying predictive relationship to comprehensively obtain the overall causal relationship, which further guarantees causal significance. The validity of the proposed method is illustrated through a real industrial example collected from a thermal power plant. © 2022 IEEE.

关键词： Information fusion

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Design of Lowpass Filter and Wavelet Analysis for Electroretinogram in Dogs

Design of Lowpass Filter and Wavelet Analysis for Electroret...

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IEEE Region 10 International Conference TENCON

作者： Tunyawat Tuntrik Nalinee Tuntivanich David Banjerdpongchai Dept. of Electrical Engineering Center of Excellence in Intelligent Control Automation of Process Systems Faculty of Engineering Chulalongkorn University Bangkok Thailand Dept. of Veterinary Surgery Faculty of Veterinary Science Bangkok Thailand

In frequency domain, wavelet coefficient is one of the characteristics of ERG signal which is hidden in frequency-domain. This paper aims to develop a tool for analyzing the constituent frequencies and time of interest in the dog's ERG signal using wavelet analysis. We design a lowpass filter to reduce noise during the measurement of ERG response. ERG dataset was obtained for the light intensity series and their constituent frequencies are analyzed by using the continuous wavelet transform (CWT). The results show that, in time-domain, as light intensity increases, both amplitudes of a-wave and b-wave are higher. We can visualize the noise interference in the ERG signals. Then a lowpass filter is designed to attenuate the noise effect. The filtered signals have lower a-, and b-waves' amplitude which affect the important characteristics in time domain. Therefore, we choose to filter signal from 26ms onward, the main components are recovered, and the filtered signal becomes smoother. In frequency-domain, the peak magnitude of wavelet coefficients is increased as light intensity increases.

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Two-Dimensional Broad Learning System for Data Analytic

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2021年第6期2卷 594-607页

作者： Dai, Wei Zhang, Rui Wang, Qianjin Cheng, Yuhu Wang, Xuesong The Engineering Research Center of Intelligent Control for Underground Space Ministry of Education China University of Mining and Technology Xuzhou221116 China The School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China The School of Electrical Engineering Yancheng Institute of Technology Yancheng221051 China

Recently, a novel learning algorithm called the broad learning system (BLS) has been established by using the theory of pseudoinverse and compressed sensing technology. As an alternative to deep learning, BLS has gained extensive attention in data analytics. Unlike the existing deep structures based learning tools (such as deep neural networks), it is able to rapidly achieve incremental learning, and does not require tedious retraining to remodel the system. However, given image data modeling tasks, 1-D BLS requires a conventional vectorization operation, the dimension of input will be quite large for high resolution image. This operation may result in huge computational complexity, thereby increasing the time of model construction. To resolve this issue, a 2-D version of BLS, namely 2-D-BLS, is first proposed to fast construct randomized learner models by using matrix inputs. Specifically, the proposed method employs left and right projecting vectors to replace the usual high dimensional input weight. Moreover, the theoretical analyses on the superiority of 2-D-BLS against BLS are presented in terms of the algorithm complexity and the difference of random parameter space. Empirical results on MNIST dataset, NORB 3-D object image recognition dataset, ORL dataset, YaleB dataset, and handwritten digit dataset demonstrate that the proposed 2-D-BLS outperforms the existing extensions of BLS and state-of-the-art 2-D machine learning methods in terms of the learning efficiency, showing good potential of 2-D-BLS for image data analytics. Impact Statement-as a novel learning tool, BLS has achieved great success in many fields. Image recognition is one of the most widely used fields of artificial intelligence. But, BLS is a 1-D model, the vectorization operation is necessary to convert matrix input data into a row or a column vector. This operation will lead to "explosion of dimensionality" and increase the time of model construction when it is coming to image data analytic

关键词： Learning systems

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Multimodal process Monitoring Based on Dynamic Extended K-means-VAE-RF

Multimodal Process Monitoring Based on Dynamic Extended K-me...

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Chinese control and Decision Conference, CCDC

作者： Shiwei Su Kaixiang Peng Xueyi Zhang University of Science and Technology Beijing Key Laboratory for Advanced Control of Iron and Steel Process of Ministry of Education School of Automation and Electrical EngineeringBeijingPeople's Republic of China100083

With the development of customization and flexibility of manufacturing industry, intelligent manufacturing process gradually presents multimodal characteristics, which are reflected in the strong coupling, nonlinear, multi working conditions and other characteristics of the process. In this paper, a multi-modal process monitoring method based on dynamic expansion is proposed. In view of the dynamic characteristics of process data, the process data is dynamically expanded through the amplification matrix, and then the multi-modal process data is modal divided through the improved K-means algorithm. The divided multi-modal process data is monitored using Variational Autoencoder, and relevant statistics and control limits are designed. Finally, the multi-modal process monitoring is constructed by introducing the method of random forest for modal identification. The validity of the algorithm is verified by real data of steel rolling process.

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Dual-Channel Wind Power Forecasting Model Using Squeeze and Excitation Network

Dual-Channel Wind Power Forecasting Model Using Squeeze and ...

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Smart Power & Internet Energy Systems (SPIES), International Conference on

作者： Haonan Li Bozhen Jiang Zhengyang Ma Hua Geng Yi Liu Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou China Department of Automation Tsinghua University Beijing China

Accurate wind power forecasting plays an increasingly important role in the field of wind power forecasting but still intractable in practice. In order to improve the accuracy of wind power forecasting, a novel model based on squeeze and excitation network (SENet) embedded in a dual-channel is proposed. The dual-channel mechanism integrates convolutional neural network and gated recurrent unit, both of which can be used without interfering with each other. SENet can increase the attention of each important features, thus improving the network efficiency. Compared with a single model, the proposed model has better feature extraction performance with the root mean square error of 29.9. The test results of wind power data based on Kaggle platform show that the proposed method outperforms several deep learning forecasting methods.

关键词： Neural networks Wind power generation Predictive models Logic gates Feature extraction Internet Convolutional neural networks

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