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Optimal Sensor Placement for Mold Level in Continuous Casting

IFAC-PapersOnLine 2024年第22期58卷 107-112页

作者： Luca Gasparini Lukas Marko Julian Landauer Andreas Kugi Stefan Fuchshumer Andreas Steinboeck Christian Doppler Laboratory for Intelligent Process Control for High-Quality Steel Products Automation and Control Institute TU Wien Vienna Austria Automation and Control Institute TU Wien Vienna Austria voestalpine Stahl GmbH Linz Austria

For high product quality in the continuous casting of steel, it is important that the liquid steel level in the mold remains constant and that its surface is fat. A slag layer on top of the liquid steel surface prevents direct optical measurement of the surface. However, knowing the mold level and surface shape is essential for quality monitoring and feedback control. The current paper addresses the optimal placement of mold-level sensors, given that their number is limited. The actual estimation of the surface shape is performed by an observer that receives mold-level measurements as input. The computed optimal sensor positions are validated based on experimental data from a full-scale laboratory water model of the continuous caster.

关键词： Steel industry continuous casting optimal sensor placement observer design observability Gramian water model

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Robust Stochastic Configuration Network Based Prediction Model of Total Nitrogen Content in Crude Oil 5

Robust Stochastic Configuration Network Based Prediction Mod...

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5th International Conference on Industrial Artificial Intelligence, IAI 2023

作者： Lu, Jun Feng, Min Ding, Jinliang Zhang, Xiaoling Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Dalian University of Technology Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education Dalian116024 China

ISBN: (纸本)9798350325294

The refineries usually process the blending crude oil at present. Blending crude oil leads to the increased uncertainties of physical properties of crude oil. This paper presents a robust stochastic configuration network (SCN) based prediction model for total nitrogen content in crude oil. First, the robust SCN based on Bayesian framework is proposed. In the Bayesian framework, the noise and outliers are assumed to be the mixture of two Gaussian distributions to improve the robustness of SCN. Then, to solve the problem that there are no analytical solutions of parameters due to the assumption of mixture of Gaussian distributions, parameters of the proposed robust SCN are estimated by the expectation maximization (EM) algorithm. Experiments using the real-world data collected from a refinery are carried out. The results indicate that the proposed approach has better accuracy and robustness. © 2023 IEEE.

关键词： Crude oil

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Command Filtered Finite/Fixed-time Heading Tracking control of Surface Vehicles

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IEEE/CAA Journal of Automatica Sinica 2021年第10期8卷 1667-1676页

作者： Zhenyu Gao Ge Guo School of Control Engineering Northeastern University QinhuangdaoQinhuangdao 066004China State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819

This paper investigates the heading tracking problem of surface vehicles with unknown model *** on finite/fixed-time control theories and in the context of command filtered control,two novel adaptive control laws are developed by which the vehicle can track the desired heading within settling time with all signals of the closed-loop system are uniformly *** effectiveness and performance of the schemes are demonstrated by simulations and comparison studies.

关键词： Command filter finite-time control fixed-time control heading tracking control surface vehicles

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State Estimator for Linear High-Dimensional Systems With State Partition and Block-Dependent Compensation

State Estimator for Linear High-Dimensional Systems With Sta...

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第35届中国过程控制会议

作者： Ke Li Shunyi Zhao Fei Liu Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Institute of Automation Jiangnan University

This paper proposes a Bayesian estimation scheme with block-dependent compensation for linear high-dimensional systems. The state vector is divided into blocks by considering the relationship of state variables in time series and physical interpretation to reduce the underlying computational cost. However, blocking states damages the original system and introduces systematic errors. Consequently, the divided state blocks are compensated by introducing distinct compensation coefficients to improve state estimation accuracy. Considering the difference and uniqueness of state blocks, the distinct compensation coefficients are adaptively adjusted and updated over time to ensure higher accuracy of state estimation. Simulation results of numerical example are used to evaluate the performance of the proposed algorithm. It shows that the proposed algorithm possesses moderate computational cost and higher estimation accuracy compared with the existing methods.

关键词： State estimation Bayesian inference Block-dependent compensation Linear high-dimensional systems

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Extended NNs-Based Adaptive Event-Triggered Asymptotic control for Nonlinear Systems Against Actuator Faults 7

Extended NNs-Based Adaptive Event-Triggered Asymptotic Contr...

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7th International Symposium on Autonomous Systems, ISAS 2024

作者： Liu, Shanlin Zhang, Yingwei Zhao, Xudong Wang, Hesheng Northeastern University College of Information Science and Engineering Shenyang China Northeastern University State Laboratory of Synthesis Automation of Process Industry Shenyang China Dalian University of Technology Faculty of Electronic Information and Electrical Engineering Dalian China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798350363173

In this article, an adaptive event-triggered asymp¬totically fault-tolerant control (FTC) issue for nonlinear systems with actuator faults is investigated. An extended neural networks (NNs) technique is introduced to estimate unknown functions of the system under consideration. Compared with traditional NNs that ignore approximation errors, the proposed extended NNs further processes approximation errors, reducing the conservatism of the proposed algorithm. Then, within the framework of backstepping method, a new adaptive control strategy is developed by combining event-triggered mechanism (ETM) and bound estimation (BE) approach, which ensures the asymptotic convergence of system states while also reducing the transmission of unnecessary information, thus improving the utilization of communication resources. At the same time, the designed controller eliminates the adverse effects of actuator failures and system nonlinear terms. Finally, a simulation example of a single-link robot system is provided to demonstrate that the developed control strategy can ensure that all signals are bounded and system states are asymptotically convergent. © 2024 IEEE.

关键词： Approximation algorithms

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Lateral-Longitudinal Prescribed-Time Cooperative control of Vehicles Subject to Wear and Road Disturbance 43

Lateral-Longitudinal Prescribed-Time Cooperative Control of ...

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43rd Chinese control Conference, CCC 2024

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

ISBN: (纸本)9789887581581

This paper studies the lateral and longitudinal formation control issue of a collection of vehicles subject to wear and road disturbance. A novel prescribed-time disturbance observer is designed to estimate the unknown disturbance. Then, based on the disturbance observer, an adaptive nonsingular vehicle formation control strategy is proposed to ensure the formation errors converge to a close neighborhood of origin in a prescribed time, which can be confirmed in advance by specifying several simple parameters. Simulation results validate the effectiveness of the presented control method. © 2024 Technical Committee on control Theory, Chinese Association of automation.

关键词： Vehicles

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Phase Fraction Estimation Using Measured Dilation, a Lattice Parameter Model, and a Curve-Fitting Method

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IFAC-PapersOnLine 2024年第22期58卷 48-53页

作者： Solveig Sannes Lukas Jadachowski Martin Niederer Andreas Kugi Andreas Steinboeck Christian Doppler Laboratory for Intelligent Process Control for High-Quality Steel Products Automation and Control Institute TU Wien Austria AIT Austrian Institute of Technology GmbH Vienna Austria

Dilatometry is a measurement frequently used to analyze phase transformations in steels during austenite decomposition. Such analyses are essential for the design of heat treatments and for determining the final phase fractions. This paper presents a simple and systematic method to estimate the evolution of the non-measurable phase fraction of ferrite, pearlite, bainite, and martensite during Continuous Cooling Transformation (CCT) experiments. The optimization-based method uses the measured dilation curve, a lattice parameter model, and a semi-empirical curve-fitting method. The latter assumes specific shapes of the transformation rates. The proposed approach can reconstruct the dilation curve and estimate the phase fractions with high accuracy.

关键词： Dilatometry austenite decomposition lattice parameter phase trajectory estimation curve-fitting method optimization-based parametrization

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Identification of Abnormal Operating Conditions in Fused Magnesium Furnaces Using a Deep Multiview TSK Fuzzy System

Identification of Abnormal Operating Conditions in Fused Mag...

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2024 China automation Congress, CAC 2024

作者： Xi, Chao Wu, Gaochang Fan, Zizhu Xu, Feng Wang, Hui Tang, Xiaokun School of Electrical and Automation Engineering East China Jiaotong University Nanchang China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China College of Computer Science and Technology Shanghai University of Electric Power Shanghai China Key Laboratory of Advanced Control and Optimization of Jiangxi Province East China Jiaotong University Nanchang China

ISBN: (纸本)9798350368604

The smelting process of the fused magnesium furnace(FMF) is prone to semi-molten abnormal conditions, which may lead to low product quality and production efficiency, and is also prone to safety accidents. At present, the operating conditions of FMF mainly rely on manual observation of the furnace shell features for diagnosis, and it is hard to timely and effectively identify abnormalities in the smelting process. Given the difficulty in identifying abnormal operating conditions of FMF, this paper proposes a deep multiview fuzzy classification method(DMFC) that can automatically identify abnormal operating conditions. First, based on the three-phase current data, CNN and Transformer are used to extract features as multiple views. Secondly, the Takagi-Sugeno-Kang(TSK) fuzzy model is used as a single-view model, and the evidence theory is used for dynamic fusion of multiple views. This method can effectively fuse multiple deep features, and has strong scalability and a certain degree of interpretability. Through verification of the real historical data of multiple FMFs, our model has relatively high accuracy, false detection rate(FDR), and missed detection rate(MDR). © 2024 IEEE.

关键词： Smelting

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Causal-Transformer: Spatial-temporal causal attention-based transformer for time series prediction

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IFAC-PapersOnLine 2024年第14期58卷 79-84页

作者： Yaqi Zhu Fan Yang Andrei Torgashov Department of Automation Beijing National Research Center for Information Science and Technology Tsinghua University Beijing 100084 China Process Control Laboratory Institute of Automation and Control Processes Far-Eastern Branch Russian Academy of Sciences Vladivostok 690041 Russia

Real-time monitoring and accurate prediction of key variables are indispensable to ensure industrial production activities proceed as expected. With the increase in measurement data volume and the improvement of hardware computing power, the Transformer and its variants, due to their excellent capability in extracting global dependencies, are playing an increasingly important role among deep learning-based multidimensional time series prediction models. In addition, from the perspective of causality, cause variables contain parts of information in effect variables and can reduce the uncertainty of effect variables, which is beneficial for prediction. However, there has been relatively limited research on combining the Transformer and causal feature analysis. To fully use both advantages, this paper introduces the Causal-Transformer (CT) model, which utilizes semi-orthogonal projection to extract causal features from multiple input variables. A multi-head spatial-temporal causal attention mechanism is designed in the encoder block based on the classical Transformer model to simultaneously reduce feature dimensions and extract implicit causal features in both the temporal and spatial dimensions. The CT also utilizes the Granger causality analysis to select the causal teaching indicators of target variables to provide stable assistance by injecting explicit causality into the inputs of the decoder block. By leveraging more condensed and independent causal features, the CT possesses inherent advantages in predicting time series variables. Case study results show that the CT model outperforms the other models on the diesel refinery dataset, especially with a reduction of 46.0% and 30.4% in MSE towards the classic Transformer and informer in five-step prediction.

关键词： Machine learning Time series modelling Transformer Causal analysis Attention machenism

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A Norm Optimal Iterative Learning control Approach with Efficient Computation 13

A Norm Optimal Iterative Learning Control Approach with Effi...

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

作者： Gao, Luyuan Zhuang, Zhihe Tao, Hongfeng Chen, Yiyang Paszke, Wojciech Jiangnan University Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Wuxi214122 China School of Mechanical and Electrical Engineering Soochow University Suzhou215137 China Institute of Automation University of Zielona Gora Zielona Gora Poland

ISBN: (纸本)9798350361674

Iterative learning control (ILC) is excellently applied to output tracking for systems that perform repetitive tasks. Classical norm optimal ILC (NOILC) approach yields optimal control inputs by minimizing a quadratic cost function. However, this approach requires the introduction of supervectors, i.e., lifting technique, for system description, which raises a high computational load problem. The contribution of this paper is to propose a non-lifted NOILC (N-NOILC) approach by redesigning a cost function that does not involve the lifting technique, achieving the goal of efficient computation. Therefore, the proposed approach is particularly suitable for large tracking tasks with long trial duration and high sampling frequency. Moreover, considering the model uncertainty in practice, robust convergence is analyzed. Numerical simulations are shown to demonstrate the properties and advantages of the proposed approach. © 2024 IEEE.

关键词： Adversarial machine learning

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