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第33届中国控制与决策会议

作者： Wei Shen Huan Liu Luwei Shao Key Laboratory of Complex System Intelligent Control and Decision School of AutomationBeijing Institute of Technology Key Laboratory of Servo Motion System Drive and Control Ministry of Industry and Information TechnologySchool of AutomationBeijing Institute of Technology

The deadbeat predictive current control(DPCC) exhibits strong dynamic performance in the current control of permanent magnet synchronous motor(PMSM),whereas the performance of the traditional DPCC depends on the accuracy of model parameters to a great *** cope with this problem,parameter sensitivity of the traditional DPCC has been analyzed in detail,with a disturbance observation compensation method proposed to improve the robustness of the current control of ***,a method of non-singular terminal sliding mode disturbance observer(NTSMDO) which employs a novel reaching law,combined with the DPCC is put forward to enhance the parameters robustness of the current loop.A harmonic compensation(HC) term has been added to the output of the current control for ignoring the harmonic electromagnetic process in the mathematical model of the PMSM to further promote the performance of the current loop *** and experimental results demonstrate that the proposed method offers robustness against parameters mismatch and control precision compared with the traditional DPCC.

关键词： PMSM Predictive current control NTSMDO Parameter mismatch Harmonic compensation

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Partially Occluded Face Expression Recognition with CBAM-Based Residual Network for Teaching Scene

Partially Occluded Face Expression Recognition with CBAM-Bas...

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Chinese automation Congress (CAC)

作者： Yanxing Bai Luefeng Chen Min Li Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration School of Automation Ministry of Education China University of Geosciences Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland Department of Computer Engineering Istinye University Sariyer/Istanbul Turkey Tokyo Institute of Technology Yokohama Japan

In this paper, a deep residual network based on convolutional block attention module (CBAM) is proposed, which is utilized for feature extraction of partially occluded face expression data. The proposed method overcomes the problem of localized occlusion face feature extraction by focusing on the regions and channels containing important information in the occluded face data through CBAM. Multi-task cascaded convolutional networks (MTCNN) are firstly utilized to localize the key regions of face emotion, and then deep emotion features are extracted by CBAM-ResNet network. The final emotion labels are generated. The effectiveness of this paper's method is verified on the RAF-DB dataset and the occluded CK+ dataset. The experimental accuracy in the RAF-DB dataset is 76.3%, which is 3.74% and 1.64% higher than the accuracy produced by the method of RGBT, and the WLS-RF, respectively. Application experiments are carried out in the real teaching scenario, which verifies the applicability of the algorithm in the real teaching scene.

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Fuzzy PID control for suppressing stick-slip vibration of drill-string system

Fuzzy PID control for suppressing stick-slip vibration of dr...

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Chinese automation Congress (CAC)

作者： Shuaikang Hu Chengda Lu Min Wu Zhaoqing He Haitao Song Wangnian Li School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China China Coal Technology and Engineering Group Xi'an Research Institute Co. Ltd Xi'an China

In this paper, a fuzzy PID (Proportional Integral Derivative) controller is introduced to suppress the stick-slip vibration present in the drilling process. First, the dynamical model of stick-slip vibration is composed of the multi-DOF (Degree-Of-Freedom) drill-string model and the Karnopp model describing the interaction of the drill bit with the rock. Then, stability analysis is made for the initial PID parameters of our controller. To suppress the stick-slip vibration and at the same time mitigate the effects of the stratigraphic environment variation on the control performance, the fuzzy adaptive algorithm is chosen as the method of optimization controller parameters. From the experimental results, it is clear that our controller has good control performance in the complex and changing stratigraphic environment.

关键词： Vibrations Drilling PI control Delay effects Process control Rocks Stability analysis

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Suppressing Stick-Slip Vibration of Drill-String system Subject to Actuator Saturation

Suppressing Stick-Slip Vibration of Drill-String System Subj...

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第40届中国控制会议

作者： Zhaoqing He Chengda Lu Min Wu Jun Gao School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education CCTEG Xi'an Research Institute

In this paper, the actuator saturation of drill-rig is considered in the mitigation of stick-slip vibration of the drill-string system. Firstly, the dynamic of stick-slip vibration is described by the combination of a multi-degree-of-freedom drill-string model and a bit-rock interaction model. To suppress stick-slip vibration while reducing the effect of saturation on control performance, a dynamic output feedback controller with an anti-windup compensator is introduced. Then, stability analysis of the resulting closed-loop system is carried out. By solving an optimization problem, the gain matrix of the anti-windup compensator is obtained. Finally, a case study is used to show the effectiveness of the method.

关键词： Drill-string system actuator saturation stick-slip vibration anti-windup compensator

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An Adaptive Model Management Strategy: Balancing Exploration and Exploitation

An Adaptive Model Management Strategy: Balancing Exploration...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Caie Hu Sanyou Zeng Changhe Li Mechanical Engineering and Electronic Information China University of Geoscience Wuhan China Automation and Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China University of Geoscience Wuhan China

ISBN: (纸本)9781728190495

Model management is a critical component in data-driven surrogate-assisted evolutionary optimization. It is mainly used for selecting candidate samples to be evaluated using exact functions to balance the exploration and exploitation of algorithms. However, there are some potential limitations for typical infill sampling criteria. An adaptive model management strategy is proposed to overcome the limitations. It adaptively adjusts the selection probability of candidate samples, and avoids potentially unreliability and the burden of selecting parameters of typical infill sampling criteria. The comparison results on benchmark problems demonstrate the competitiveness of the strategy in solving data-driven optimization problems.

关键词： Adaptation models Toy manufacturing industry Reinforcement learning Evolutionary computation Benchmark testing Linear programming Optimization

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Design and Development of Big Data system for Blast Furnace Ironmaking

Design and Development of Big Data System for Blast Furnace ...

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Industrial Cyber-Physical systems (ICPS)

作者： Qifu Chen Jinhua She Min W Zhuofu Zhang Zhenhong Liao School of Future Technology China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration. Ministry of Education Wuhan China Hunan Valin Lianyuan Iron and Steel Co. Ltd. Loudi China School of Engineering Tokyo University of Technology Tokyo Japan School of Automation. China University of Geosciences Wuhan China

The process of blast furnace ironmaking is com-posed of several sub-processes. The process of blast furnace smelting is complicated and the data of each sub-process are scattered and not concentrated. The traditional working mode of subjective judgment based on manual experience is not conducive to the stable production of the blast furnace. Therefore, this paper develops a big data system for the production process of blast furnace, which is of great significance to realize the digital production and intelligent production of blast furnace. According to the system architecture of blast furnace ironmaking, this paper mainly designs and develops the data acquisition layer, data storage layer, data analysis layer and data application layer of the big data system.

关键词： Software design Data acquisition Smelting Memory systems architecture Production User interfaces

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Vehicle detection based on point cloud intensity and distance clustering 5

Vehicle detection based on point cloud intensity and distanc...

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2020 5th International Seminar on Computer Technology, Mechanical and Electrical Engineering, ISCME 2020

作者： Wang, Zhao Wang, Xing Fang, Bin Yu, Kun Ma, Jie School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China National Key Laboratory of Multispectral Information Processing Technology Huazhong University of Science and Technology Wuhan430074 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

In the intelligent transportation system, vehicle detection is one of the essential technologies in obstacle avoidance and navigation, however the existing vehicle detection methods cannot meet the actual needs. This paper presents a vehicle detection method combines the intensity and distance information of point cloud, which improves the segmentation performance of nearby objects. Specifically, the data of point cloud collected by lidar is preprocessed first. Then the processed point cloud is clustered by combining its coordinate and intensity information. Finally, the clustered suspected targets are fed to the random forest classifier. Our method can efficiently detect and classify targets in large-scale disordered 3D point cloud with high accuracy. In the real-scanned Livox Mid-40 Lidar dataset, our proposed method improves the detection accuracy by 31% compared with the traditional Euclidean clustering. © Published under licence by IOP Publishing Ltd.

关键词： Optical radar

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Anomaly Detection Based on Principle of Justifiable Granularity and Probability Density Estimation

Anomaly Detection Based on Principle of Justifiable Granular...

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Chinese automation Congress (CAC)

作者： Sheng Du Xian Ma Xiang Li Min Wu Weihua Cao Witold Pedrycz School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland Department of Computer Engineering Faculty of Engineering and Natural Sciences Istinye University Sariyer/Istanbul Turkiye

Anomaly detection is essential to ensure the safety of industrial processes. This paper presents an anomaly detection approach based on the probability density estimation and principle of justifiable granularity. First, time series data are transformed into a two-dimensional information granule by the principle of justifiable granularity. Then, the test statistic is constructed, and the probability density and cumulative distribution functions of the test statistic are calculated. Next, the confidence level determines the test threshold. Finally, the time series data of a key parameter in the sintering process is used as a case study. The experimental result demonstrates that the proposed approach can detect abnormal time series data effectively, providing an accurate and effective solution for detecting time series anomalies in industrial processes.

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Multi-objective Evolutionary Algorithm Based Graph Neural Network Architecture Search 11th

Multi-objective Evolutionary Algorithm Based Graph Neural Ne...

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11th International Symposium on Computational Intelligence and Industrial Applications, ISCIIA 2024

作者： He, Lianyi Liu, Xiaobo Xiang, Hongbo Wang, Guangjun School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China Key Laboratory of Geological Survey and Evaluation of Ministry of Education China University of Geosciences Wuhan430074 China

ISBN: (纸本)9789819647521

Graph Neural Networks (GNN) has become a powerful graph data processing method, which has been widely used in node classification, link prediction, and other graph analysis tasks. Due to the diversity and complexity of graph structures and information propagation, as well as the handling of heterogeneous graphs, etc., the design of GNNs presents many challenges. The existing methods have high classification accuracy, but their structures are very complex. In this paper, a graph neural network architecture search framework named MO-GNN, which is based on multi-objective evolutionary algorithm is proposed. Furthermore, a weight sharing strategy MO-GNN-WS is proposed to reduce the resource consumption caused by weight training of different architectures. To verify the performance of the proposed algorithm, experiments on four popular graph datasets are used for transduction and induction tasks. The experimental results show that the MO-GNN-WS algorithm outperforms the most advanced neural network architecture search methods in terms of classification accuracy and resource consumption. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data Sharing

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Long and Short-Term Constraints Driven Safe Reinforcement Learning for Autonomous Driving

arXiv

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arXiv 2024年

作者： Hu, Xuemin Chen, Pan Wen, Yijun Tang, Bo Chen, Long School of Artificial Intelligence Hubei University Wuhan430062 China Key Laboratory of Intelligent Sensing System and Security Hubei University Ministry of Education Hubei Wuhan430062 China Department of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA01609 United States State Key Laboratory of Multimodal Artificial Intelligence Systems State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing100190 China WAYTOUS Inc. Beijing100083 China Shenzhen518107 China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi’an710049 China

Reinforcement learning (RL) has been widely used in decision-making and control tasks, but the risk is very high for the agent in the training process due to the requirements of interaction with the environment, which seriously limits its industrial applications such as autonomous driving systems. Safe RL methods are developed to handle this issue by constraining the expected safety violation costs as a training objective, but the occurring probability of an unsafe state is still high, which is unacceptable in autonomous driving tasks. Moreover, these methods are difficult to achieve a balance between the cost and return expectations, which leads to learning performance degradation for the algorithms. In this paper, we propose a novel algorithm based on the long and short-term constraints (LSTC) for safe RL. The short-term constraint aims to enhance the shortterm state safety that the vehicle explores, while the long-term constraint enhances the overall safety of the vehicle throughout the decision-making process, both of which are jointly used to enhance the vehicle safety in the training process. In addition, we develop a safe RL method with dual-constraint optimization based on the Lagrange multiplier to optimize the training process for end-to-end autonomous driving. Comprehensive experiments were conducted on the MetaDrive simulator. Experimental results demonstrate that the proposed method achieves higher safety in continuous state and action tasks, and exhibits higher exploration performance in long-distance decision-making tasks compared with state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Lagrange multipliers

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