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第40届中国控制会议

作者： Jiangxiao Wu Sen Zhang Wendong Xiao School of Automation University of Science and Technology Beijing Key Laboratory of Advanced Control of Iron and Steel Process (Ministry of Education) University of Science and Technology Beijing

Burden distribution is an important method to regulate the operation state of blast furnace. Reasonable burden distribution matrix of blast furnace is the prerequisite for improving resource utilization and ensuring stable blast furnace *** order to achieve the above target, an ensemble prediction model based on weighted voting and a method of calculating weights are proposed in this paper. In this model, the convolution neural network（CNN）-bidirectional gated recurrent unit（BiGRU）based on attention mechanism, logistic regression, k-nearest neighbor（KNN） and support vector machines（SVM） are integrated to predict whether the burden distribution matrix of blast furnace needs to be adjusted. The weight of each individual model is determined by its prediction accuracy and F1-score. CNN converts the extracted feature vectors into time series and inputs them into BiGRU. The attention mechanism is introduced to assign different weights to the hidden state of BiGRU, so as to enhance the long-term influence of important information. Finally, the production data of blast furnace are used to testify the performance of prediction models. In this paper, Multilayer Perceptron（MLP） model, RNN model, BiGRU model, BiGRU model based on attention mechanism, logistic regression model, KNN model, SVM model and the proposed ensemble model are used for comparison experiments, compared with other prediction models, the experimental results illustrated that the weighted voting ensemble prediction model can accurately determine whether the burden distribution matrix of blast furnace needs to be adjusted and have better prediction accuracy and generalization performance.

关键词： Blast furnace Burden distribution matrix Ensemble model Attention mechanism BiGRU

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Two Feature Fusion Network Based on Efficient Deep Residual Shrinkage Spatial-Temporal Graph Convolution for Emotion Recognition from Gaits

Two Feature Fusion Network Based on Efficient Deep Residual ...

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第34届中国控制与决策会议

作者： Xiaoqing Liu Sen Zhang Wendong Xiao School of Automation and Electrical Engineering University of Science and Technology Beijing Key Laboratory of Advanced Control of Iron and Steel Process (Ministry of Education) Shunde Graduate School University of Science and Technology Beijing Beijing Engineering Research Center of Industrial Spectrum Imaging

Emotion Recognition plays a key role in human social *** current methods choose facial information for emotion *** facial information is easily limited by distance in large *** information can break this limitation,but the amount of publicly available data is *** paper proposes two feature fusion network based on efficient deep residual shrinkage spatial-temporal graph convolution to achieve automatic emotion recognition from gaits in large *** chebyshev polynomial approximation graph convolution is combined with efficient deep residual shrinkage network to obtain efficient deep residual shrinkage spatial-temporal graph convolution network to extract deep *** sixteen temporal affective features extracted from gaits are used as input of efficient deep residual shrinkage network to extract new affective *** the deep features is spliced with new affective features to divided emotion into happy,angry,sad and *** performed a ten-flod cross-validation experiment on the E-gait real *** highest recognition accuracy is 0.87,with an average of *** results show that the proposed model is effective in dealing with emotion recognition promblem from gait in fewer sample situation.

关键词： emotion recognition from gaits chebyshev polynomial approximation deep residual shrinkage Network efficient channel attention mechanism

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Nonlinear Cooperative Output Regulation with Input Delay Compensation

arXiv

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arXiv 2024年

作者： Zheng, Shiqi Ahn, Choon Ki Jiang, Xiaowei Yan, Huaicheng Shi, Peng The School of Automation China University of Geosciences Wuhan430079 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China The Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China The Shaoyang Institute of Advanced Manufacturing Technology Shaoyang422000 China The School of Electrical Engineering Korea University Seoul136-701 Korea Republic of The Key Laboratory of Smart Manufacturing in Energy Chemical Process of Ministry of Education East China University of Science and Technology Shanghai200237 China The School of Electrical and Mechanical Engineering The University of Adelaide AdelaideSA5005 Australia

This paper investigates the cooperative output regulation (COR) of nonlinear multi-agent systems (MASs) with long input delay based on periodic event-triggered mechanism. Compared with other mechanisms, periodic event-triggered control can automatically guarantee a Zeno-free behavior and avoid the continuous monitoring of triggered conditions. First, a new periodic event-triggered distributed observer, which is based on the fully asynchronous communication data, is proposed to estimate the leader information. Second, a new distributed predictor feedback control method is proposed for the considered nonlinear MASs with input delay. By coordinate transformation, the MASs are mapped into new coupled ODE-PDE target systems with some disturbance-like terms. Then, we show that the COR problem is solvable. At last, to further save the communication resource, a periodic event-triggered mechanism is considered in the sensor-to-controller transmission in every agent. A new periodic event-triggered filter is proposed to deal with the periodic event-triggered feedback data. The MASs with input delay are mapped into coupled ODE-PDE target systems with sampled data information. Then, Lyapunov-Krasovskii functions are constructed to demonstrate the exponential stability of the MASs. Simulations verify the validity of the proposed results. © 2024, CC BY-NC-ND.

关键词： Feedback control

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Distributed Energy Seheduling of Islanded Microgrid Based on Potential Game

Distributed Energy Seheduling of Islanded Microgrid Based on...

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2020 Chinese automation Congress, CAC 2020

作者： Chen, Lei Niu, Yugang Jia, Tinggang East China University of Science and Technology The Key Laboratory of Advanced Control and Optimization for Chemical Process Shanghai China Shanghai Electric Automation Group Shanghai200023 China

ISBN: (纸本)9781728176871

Microgrid is an advanced application of distributed renewable energy utilization. With the development of energy Internet technology, microgrid is characterized by strong decentralization and high intelligence. The operation optimization of microgrid requires higher individual autonomy and rational ability, and the traditional centralized optimization is difficult to meet this development trend. In this paper, in order to better reflect the high autonomy of microgrid energy management, a distributed optimization based on potential game is proposed by comprehensively considering multiple independent individuals in the microgrid. Under this strategy, each individual makes decisions with the goal of maximizing their own profits, and completes the iterative solution of the strategy through communication with each other. Finally, a simulation example of islanded microgrid is used to verify the results, which shows that the individual autonomy and intelligence of the microgrid were improved, and the feasibility and effectiveness of the strategy are verified. © 2020 IEEE.

关键词： Energy management

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Improved LMI-based conditions for designing of PD-type ILC laws for linear batch processes over two-dimensional setting

Improved LMI-based conditions for designing of PD-type ILC l...

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International Conference on control, automation, Robotics and Vision (ICARCV)

作者： Robert Maniarski Wojciech Paszke Hongfeng Tao Shoulin Hao Institute of Automation Electronic and Electrical Engineering University of Zielona Góra Zielona Góra Poland Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Jiangnan University Wuxi China Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian China

ISBN: (纸本)9781665476881

This paper considers the problem of designing of iterative learning control (ILC) laws for linear batch processes. Unlike the majority of existing results about ILC law design for linear batch processes over repetitive/two-dimensional setting where Lyapunov theory is applied, this study is focused on formulating the ILC law design procedures by transforming it into an equivalent problem of (structural) stability analysis for a linear Roesser model for two-dimensional (2D) systems. Then, based on a non-conservative version of stability and stabilization conditions for linear 2D systems, suitable PD-type ILC laws are derived by the application of the linear matrix inequality (LMI) approach. Finally, a numerical example is given to show the validity of the proposed design procedure and some advantages are emphasized when compared to the existing alternatives.

关键词： State feedback Uncertainty Two dimensional displays Batch production systems Stability analysis Structural engineering Numerical models

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Linear parameter varying control of wind energy conversion systems in partial load 19

Linear parameter varying control of wind energy conversion s...

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19th International Conference on Machine Learning and Cybernetics, ICMLC 2020

作者： Xu, Jing Sun, Qing East China University of Science and Technology Ministry of Education The Key Laboratory of Advanced Control and Optimization for Chemical Process Shanghai200237 China Shanghai University Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronical Engineering and Automation Shanghai200072 China

ISBN: (纸本)9780738124261

This paper is dealt with the rotor speed tracking problem of variable-speed wind turbine systems operating under the partial load condition. Singular perturbation techniques are used to characterize the two-time-scale property of the wind turbine system, and a linear parameter varying (LPV) model is formed to approximate nonlinear behaviours of a wind turbine system. Based on the slow-fast decomposition method, slow and fast subsystems are constructed: one for the mechanical dynamics and the other for the electrical dynamics. Slow and fast controls are derived, respectively, and then a local state feedback controller, sum of the slow and fast control, is formulated. A design procedure, using the linear parameter varying control to combine local controllers, is proposed to guarantee the robustness of the closed-loop nonlinear wind turbine system. Numerical examples are given to show the validity of the proposed control scheme. © 2020 IEEE.

关键词： Wind turbines

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

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

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Data Driven control and Learning Systems (DDCLS)

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

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

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.

关键词： Fault diagnosis Learning systems Employee welfare Sensitivity Power supplies Noise Electric vehicles

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Two Feature Fusion Network Based on Efficient Deep Residual Shrinkage Spatial-Temporal Graph Convolution for Emotion Recognition from Gaits

Two Feature Fusion Network Based on Efficient Deep Residual ...

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Chinese control and Decision Conference, CCDC

作者： Xiaoqing Liu Sen Zhang Wendong Xiao School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Key Laboratory of Advanced Control of Iron and Steel Process (Ministry of Education) Beijing China Shunde Graduate School University of Science and Technology Beijing Foshan China Beijing Engineering Research Center of Industrial Spectrum Imaging Beijing China

ISBN: (纸本)9781665478977

Emotion Recognition plays a key role in human social activities. Most current methods choose facial information for emotion recognition. But facial information is easily limited by distance in large scenes. Gait information can break this limitation, but the amount of publicly available data is small. This paper proposes two feature fusion network based on efficient deep residual shrinkage spatial-temporal graph convolution to achieve automatic emotion recognition from gaits in large scenes. Firstly chebyshev polynomial approximation graph convolution is combined with efficient deep residual shrinkage network to obtain efficient deep residual shrinkage spatial-temporal graph convolution network to extract deep features. Then sixteen temporal affective features extracted from gaits are used as input of efficient deep residual shrinkage network to extract new affective features. Finally the deep features is spliced with new affective features to divided emotion into happy,angry,sad and neutral. We performed a ten-flod cross-validation experiment on the E-gait real datasets. The highest recognition accuracy is 0.87, with an average of 0.85. Experimental results show that the proposed model is effective in dealing with emotion recognition promblem from gait in fewer sample situation.

关键词： Emotion recognition Convolution Chebyshev approximation Feature extraction

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Passive mechanical realizations of bicubic impedances with no more than five elements for inerter-based control design

arXiv

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arXiv 2021年

作者： Wang, Kai Chen, Michael Z.Q. Key Laboratory of Advanced Process Control for Light Industry Ministry of Education School of Internet of Things Engineering Jiangnan University Wuxi214122 China School of Automation Nanjing University of Science and Technology Nanjing210094 China

This paper mainly investigates the passive realization problems of bicubic (third-order) impedances as damper-spring-inerter networks consisting of no more than five elements. First, the special case where a bicubic impedance contains a pole or a zero on the imaginary axis or at infinity is discussed. Then, assuming that there is no pole or zero on the imaginary axis or at infinity, the realizations of bicubic impedances as five-element networks are investigated. Necessary and sufficient conditions for the realizability as five-element series-parallel networks and as five-element non-series-parallel networks are derived, respectively, where 22 series-parallel configurations and 11 non-series-parallel configurations are presented to cover the conditions. Finally, two numerical examples together with positive-real controller designs for a quarter-car suspension system are presented for illustrations. The results of this paper can contribute to the synthesis of low-complexity passive mechanical (or electrical) networks, which are motivated by the synthesis and design of inerter-based vibration control systems. Copyright © 2021, The Authors. All rights reserved.

关键词： Poles

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Submodularity-based sensor scheduling for state estimation among multiple linear dynamical systems

Submodularity-based sensor scheduling for state estimation a...

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Jiangsu Annual Conference on automation (JACA 2022)

作者： Y. Ni C. Yang X. Liu X. Luan F. Liu The Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) School of Internet of Things Engineering Jiangnan University Wuxi People's Republic of China The Key Laboratory of Smart Manufacturing in Energy Chemical Process (Ministry of Education) Department of Automation East China University of Science and Technology Shanghai People's Republic of China Department of Radiology Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital/Harvard Medical School Boston USA

In this paper we focus on the sensor scheduling for dynamic state estimation among multiple processes. Multiple sensors transmit their measurements to the remote estimator via a shared wireless channel. Only a subset of sensors are allowed to transmit at each time. The objective of the scheduler is to maximize a linear combination of the terminal time estimation error covariances in a log-determinant sense. We develop conditions under which the objective is monotone and submodular. A submodularity-based greedy scheduling algorithm is proposed, and its performance bound is provided. Numerical examples are presented to illustrate the theoretical findings.

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