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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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Underwater Sludge Detection System Based on Multi-Data Fusion

Underwater Sludge Detection System Based on Multi-Data Fusio...

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International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS)

作者： Yunchao Jiang Shunyi Zhao Songjie Guo Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Jiangnan University Wuxi China Information Center Hangzhou Yuhang Water Co. Ltd Hangzhou China

ISBN: (纸本)9781665402460

Underwater sludge greatly affects water quality, and it is an important task of urban maintenance to clean up the sludge in the urban rivers. To remove sludge, a critical step is to measure the location and depth of sludge accurately, which leads to a refined calculation of maintenance fee. A traditional method of measuring sludge is based on human experience, which is costly, and more importantly, calculation errors and low efficiency. In this paper, we introduce an intelligent and automatic underwater sludge detection system. Two sonars equipped on an unmanned ship provide the measurements of depth, which are fused using the Kalman-like method to obtain accurate measures of sludge depth. Our experiment shows that the system can detect sludge depth conveniently.

关键词： Performance evaluation Fuses Sonar measurements Measurement uncertainty Sonar Water quality Maintenance engineering

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Notice of Removal: Active disturbance rejection control for nonlinear two-tank liquid level cascade systems with model uncertainties

Notice of Removal: Active disturbance rejection control for ...

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SICE Annual Conference

作者： Lijun Wang Fen Zhang Qing Li Yixin Yin Qing Zheng Yumeng Zhang Key Laboratory of Advanced Control of Iron and Steel Process (Ministry of Education) University of Science and Technology Beijing Beijing China Department of Electrical and Computer Engineering Gannon University Pennsylvania USA

Due to the nonlinearities, model uncertainties and disturbances of the two-tank liquid level cascade system, active disturbance rejection controller (ADRC) solution was developed. First, a nonlinear mechanism model and its linearized model were built based on the theories of fluid dynamics for the system. Second, first-order linear ADRCs for the outer loop of the two cascade systems with an unknown order were designed, respectively. With a simple integrator as the normalized type of the feedback system, other parts of dynamic system which are different from the normalized type are treated as the total disturbances. The disturbances were estimated by the second-order linear extended state observer and compensated in the control law. Then the system, with nonlinearities, uncertainties and disturbances, is reduced to only a linear integrator. In comparison with the traditional PI controller, simulation results show that both the proposed ADRCs have better robustness and adaptability against model uncertainties.

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Exponential Weighted Moving Average-Based Variational Mode Decomposition Method for Fault Diagnosis of Vehicle Batteries 13

Exponential Weighted Moving Average-Based Variational Mode D...

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13th IEEE Data Driven control and Learning Systems Conference, DDCLS 2024

作者： Wu, Penghao Tao, Hongfeng Tian, Engang Yin, Tangwen Chen, Hongtian Chen, Yiyang School of Mechanical and Electrical Engineering Soochow University Suzhou215137 China Jiangnan University Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Wuxi214122 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Shanghai Jiao Tong University Department of Automation Shanghai200240 China

ISBN: (纸本)9798350361674

In recent years, the rapid advancement of electric vehicles has heightened concerns regarding the safety of high-energy batteries. Consequently, there has been a significant focus on the development of fault diagnosis technologies for batteries. Intelligent fault diagnosis (IFD), particularly data-driven approaches, represents a crucial methodology in addressing this concern. This paper proposes a data-driven approach, for IFD in high-energy batteries utilized in electric vehicles. The proposed Exponentially Weighted Average Variational Mode Decomposition (EWVMD) method overcomes these limitations by employing the exponential weighted moving average (EWMA) technique to smooth collected time-series voltage data. This process reduces noise, accentuates abnormal change trends, and yields secondary data with enhanced features. Subsequently, variational mode decomposition (VMD) is applied to the secondary data. The resulting decomposed sub-mode curves (intrinsic mode functions - IMFs) and the change trends of the residual curve, in conjunction with a fault threshold tailored to actual working conditions, are utilized to ascertain the occurrence of faults. Design verification experiments, the C42MSA electric vehicle power supply battery undergoes charging and discharging tests with injected faults at fixed intervals. The collected fault signals are then subjected to fault diagnosis using EWVMD. Results demonstrate the algorithm's superior detection effectiveness and increased sensitivity to fault signals compared to traditional methods. © 2024 IEEE.

关键词： Variational mode decomposition

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Data-Driven Distributionally Robust Planning of Energy Hub Coupled with Carbon Capture Utilization and Storage Facilities

Data-Driven Distributionally Robust Planning of Energy Hub C...

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Chinese control Conference (CCC)

作者： Xutao Ma Chao Ning Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai China

Toward carbon peaking and carbon neutrality goals in China, a transition from traditional fossil power to renewable energy becomes an imperative need. This paper aims to address the planning problem of a highly renewable-energy penetrated energy hub system, coupled with carbon capture facilities and downstream production processes of hydrogen and methanol, under the uncertainty of renewable power output. To this end, we propose a two-stage distributionally robust optimal planning model, where investment decisions are the first-stage variables and operation variables are determined at the second stage after the realization of uncertainty. In the proposed model, we utilize historical data samples to construct a strengthened ambiguity set that leverages both Wasserstein distance and moment information for a better characterization of renewable generation uncertainty. Moreover, to efficiently solve the proposed planning problem, we develop a simplified affine decision rule method in a general setting with both moment and Wasserstein ambiguity, and then apply it to reformulate the distributionally robust planning problem. The derived reformulation possesses the favorable property that its scale is free from the sample size, thus greatly reducing the computational complexity. Computational results validate the effectiveness of the proposed planning model and the computational efficiency of the solution methodology.

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A multi-innovation stochastic gradient parameter estimation algorithm for controlled autoregressive ARMA systems based on the data filtering

A multi-innovation stochastic gradient parameter estimation ...

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International Conference on Information Science and Technology (ICIST)

作者： Shijun Wang Rui Ding Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Jiangnan University Wuxi 214122 China School of Internet of Things Engineering Jiangnan University Wuxi 214122 China

ISBN: (纸本)9781467327626

This paper decomposes a controlled autoregressive autoregressive moving average (CARARMA) system into two subsystems, uses the data filtering technique to drive a multi-innovation stochastic gradient algorithm for identifying the parameters of each subsystems. The basic idea is to replace the unknown variables in the information vectors with their corresponding estimates. The simulation example shows that the proposed algorithms can work well.

关键词： Signal processing algorithms Stochastic processes Mathematical model Parameter estimation Computational modeling Autoregressive processes Least squares approximations

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Performance Evaluation and Order Selection for Pattern Modeling Methods Through Granger Causality Analysis

Performance Evaluation and Order Selection for Pattern Model...

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Data Driven control and Learning Systems (DDCLS)

作者： Shaotong Zhu Niannian Zheng Xiaoli Luan Fei Liu Jiangnan University Wuxi P. R. China Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Wuxi P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In the realm of processes modeling and control, the monitoring of processes is crucial for ensuring safety, and a key task in multivariate statistical process monitoring is to extract the operating patterns of the process. Dynamic strategies, exemplified by dynamic-inner principal components analysis (DIPCA), dynamic-inner partial least squares (DIPLS), and dynamic-inner canonical correlation analysis (DICCA), are employed to tackle temporal correlation and the ever-changing nature of processes using AR structures. Dynamic controlled principal components analysis (DCPCA) additionally considers dynamic controlled characteristics, showcasing remarkable accuracy in both the monitoring of processes and dynamic prediction. Notwithstanding the precision demonstrated by DCPCA, the task of ascertaining optimal control input orders remains intricate, often necessitating reliance on empirical methodologies or trial-and-error approaches. This research introduces an innovative methodology, drawing inspiration from Granger causality analysis, to establish causal relationships between control inputs and patterns. Causal influence (CI) was employed to assess the impact of introducing control inputs and the effects of control inputs at different orders, aiding the model in selecting the optimal control input order. Theoretically supported performance evaluation and order selection, providing assurance for practical applications.

关键词： Performance evaluation process monitoring Correlation process modeling Cause effect analysis process control Optimal control

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Nonlinear system identification with multiple and correlated scheduling variables *

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IFAC Proceedings Volumes 2013年第32期46卷 319-324页

作者： Lei Chen Biao Huang Fei Liu Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Institute of Automation Jiangnan University Wuxi 214122 P.R. China Department of Chemical and Materials Engineering University of Alberta Edmonton AB T6G 2G6 Canada

This paper is concerned with identification of nonlinear systems with multiple and correlated scheduling variables. Multiple auto regressive exogenous (ARX) models are identified on different process operating conditions, and a normalized exponential function as the probability density function associated with each of the local ARX models taking effect is then used to combine all the local models to represent the complete dynamics of a nonlinear system. The parameters of the local ARX models and the exponential functions are estimated simultaneously under the framework of the expectation maximization (EM) algorithm. A numerical example is applied to demonstrate the proposed identification method.

关键词： System identification Nonlinear process Multiple models Expectation maximization algorithm Multiple scheduling variables

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H∞Asynchronous control for Discrete-Time Semi-Markov Jump Systems 16th

H∞Asynchronous Control for Discrete-Time Semi-Markov Jump S...

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16th IFAC Symposium on Large Scale Complex Systems: Theory and Applications LSS 2022

作者： Jin, Liqiang Yin, Yanyan Liu, Yanqing Teo, Kok Lay Liu, Fei National Key Laboratory of Advanced Process Control for Light Industry Ministry of Education Institute of Automation Jiangnan University Wuxi214122 China School of Electrical Engineering Computing and Mathematical Sciences Curtin University PerthWA6102 Australia School of Mathematical Sciences Sunway University Kuala Lumpur47500 Malaysia Tianjin University of Finance and Economics Tianjin China

This paper investigates the problem of H∞asynchronous control for discrete-time semi-Markov jump systems. In practice, it is difficult to ensure that controller modes and system modes are updated synchronously. Therefore, a hidden semi-Markov model is introduced to characterize such an asynchronous phenomenon between system modes and controller modes. Meanwhile, the switching signal of the controller is generated by a given conditional probability related to the system mode. Based on the mode-dependent and elapsed-time-dependent Lyapunov function, together with the semi-Markov kernel (SMK) method, sufficient conditions constructed by linear matrix inequalities (LMIs) are derived to ensure the existence of an asynchronous controller such that the resulting system is σ-error mean square stable and satisfies an H∞performance index. Finally, a numerical example is given to illustrate the effectiveness and feasibility of the method proposed. © 2022 Elsevier B.V.. All rights reserved.

关键词： controllers

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Multi-model approach to nonlinear system identification with unknown time delay

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IFAC Proceedings Volumes 2014年第3期47卷 9388-9393页

This paper is concerned with identification of nonlinear systems with a noisy scheduling variable, and the measurement of the system has an unknown time delay. Auto regressive exogenous (ARX) models are selected as the local models, and multiple local models are identified along the process operating points. The dynamics of a nonlinear system are represented by associating a normalized exponential function with each of the ARX models; therein, the normalized exponential function is acted as the probability density function. The parameters of the ARX models and the exponential functions as well as the unknown time delay are estimated simultaneously under the expectation maximization (EM) algorithm using the retarded input-output data. A CSTR example is given to verify the proposed identification approach.

关键词： Nonlinear system identification Expectation maximization algorithm Multiple models Time delay

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