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第42届中国控制会议

作者： Hao Sun Chengda Lu Zhaoqing He Shengnan Tian Zhejiaqi Ma Min Wu 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

This paper uses the wave equation to explain the torsional motion of the drill-string system.S olving the wave equation with the D'Alembert method,a neutral time-delay model of the drill-string system is *** disturbance input,caused by the bit-rock interaction,is given consideration,and an equivalent-input-disturbance(EID) based controller is designed to mitigate the disturbance in the established *** the actual drilling procedure,the system input time-delay increases as the length of the drill columns *** the influence of system input time-delay in the drilling procedure is ignored,it will most likely lead to the drill-string system instability and cause serious *** essential contribution of this paper is the incorporation of input time-delay into the EID based control *** the system's input time-delay,the proposed model is more practical and has significant implications for stick-slip vibration assessment and control in drilling procedures.

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Energy-aware Coverage control of Multi-Agent systems with control Barrier Function

Energy-aware Coverage Control of Multi-Agent Systems with Co...

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第42届中国控制会议

作者： Chengkai Qu Chao Zhai 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

Geo-hazards have become one of the main disasters endangering the safety of people's lives and property in the *** order to improve the early warning of disasters, a persistent monitoring method of multi-agent systems is proposed in this *** ensure that the agent's energy is never exhausted, the set invariance constraint is included in the optimization problem. The goal is to minimize the difference between the actual control input of the robot and the nominal control input corresponding to the task to be performed. Moreover, the control barrier function(CBF) is used to transform the forward invariance of a subset of the robot state space into a control input constraint. The coverage control method in an uncertain environment is verified by numerical simulation. This work provides new insights into effective monitoring and early warning of geo-hazards.

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H∞ consensus for stochastic Markov jump multi-agent systems with imperfect time-varying transition probabilities and multiplicative noise

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Applied Mathematics and Computation 2023年 436卷

作者： Wang, Jie Chen, Xin School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China

In this paper, the H∞ consensus control problem for Markov jump multi-agent systems with imperfect time-varying transition probabilities is studied. Both the transition probability matrix and the higher-level transition probability matrix are considered to be partly unknown. What's more, the multiplicative noise is considered in the model to describe the factors from the changing environment. Sufficient conditions about state feedback controllers are derived by less linear matrix inequalities (LMIs) and are more convenient to program compared with the previous works. At last, the simulations are supplied to illustrate the effectiveness of the conclusions. © 2022

关键词： Multi agent systems

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Robust control of Feeding Speed for Coal Mine Tunnel Drilling Machines 25

Robust Control of Feeding Speed for Coal Mine Tunnel Drillin...

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25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Liu, Xiao Chen, Luefeng Wu, Min Cao, Weihua Lu, Chengda Pedrycz, Witold School of Automation China University of Geosciences Wuhan China School of Automation The Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems The Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University Department of Computer Engineering Sariyer Istanbul34396 Turkey

Changes in coal seam hardness cause fluctuations in the feed resistance at the drill bit during the drilling process, leading to unstable feeding speed. This paper proposes a robust dynamic output feedback controller to suppress disturbances caused by the variations in coal seam hardness in the feed system. Firstly, an unknown parameter measuring coal seam hardness is introduced, and an uncertain model of the feeding system is established based on the finite element model of the drill string. By designing weighted functions based on industrial field requirements and constructing a generalized plant, the controller achieves loop shaping, reducing the low-frequency impact of coal seam hardness variations on the feed system and suppressing the systems resonance peak. Simulation results demonstrate that the controller effectively suppresses parameter variations and external disturbances caused by changes in coal seam hardness, achieving stable control of the drilling speed. © 2024 IEEE.

关键词： Robust control

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Pre-Trained Language Model - Based Real-Time Formation Lithology Identification Method for Drilling Process

Pre-Trained Language Model - Based Real-Time Formation Litho...

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Chinese automation Congress (CAC)

作者： Jianyang Liu Chao Gan Weihua Cao 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

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Geological exploration is essential for economic and social progress, with lithology identification being a key technology that improves exploration precision and resource development efficiency. This paper presents a real-time identification method of formation lithology in drilling process based on pre-training language model, which is divided into three stages. In this first stage, the drilling data undergoes rigorous cleaning, feature selection, and normalization to eliminate noise and anomalies. In the second stage, key model inputs such as weight on bit (WOB), rate of penetration (ROP), rotation speed (RPM), torque, and pump volume (P) are identified through detailed drilling mechanism analysis. In the last stage, a real-time identification method of formation lithology in drilling process based on pre-training language model is proposed, which realizes high-precision real-time identification in drilling process. The effectiveness of the proposed method is proved by comparison with common methods.

关键词： Drilling Solid modeling Accuracy Torque Geology Transforms Reliability engineering Data models Real-time systems Robustness

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Research on Temperature Error Calibration Method of Fluxgate Sensor

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Journal of advanced Computational Intelligence and intelligent Informatics 2025年第3期29卷 583-591页

作者： Yang, Ruiping Ge, Jian Luo, Wang Hu, Xiangyun She, Jinhua Chugo, Daisuke Dong, Haobin School of Automation China University of Geosciences No.388 Lumo Road Hongshan District Hubei Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems No.388 Lumo Road Hongshan District Hubei Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education No 388 Lumo Road Hongshan District Hubei Wuhan430074 China School of Engineering Kwansai Gakuin University 1 Gakuen Uegahara Hyogo Sanda669-1330 Japan School of Geophysics and Geomatics China University of Geosciences No.388 Lumo Road Hongshan District Hubei Wuhan430074 China Hubei Subsurface Multi-scale Imaging Key Laboratory No.388 Lumo Road Hongshan District Hubei Wuhan430074 China School of Engineering Tokyo University of Technology 1404-1 Katakuramachi Hachioji Tokyo192-0982 Japan

The fluxgate sensor is the most widely used sensor in vector magnetic measurement. However, during long-term continuous observation, the fluxgate sensor will produce large measurement errors due to changes in ambient temperature. This paper proposes a temperature calibration method for the fluxgate sensor based on polynomial fitting to address the temperature error of fluxgate sensors. First, the effect of temperature on the performance indicators of the fluxgate sensor was analyzed. Second, according to the existing temperature-magnetic field data, a temperature calibration model of the fluxgate sensor was constructed. Compared with other temperature calibration methods, the result shows that the proposed temperature calibration method is relatively simple and can better achieve real-time calibration for sensor application scenarios. Finally, to verify the effectiveness of the proposed method, numerous laboratory experiments were implemented. The temperature drift was reduced from about 700 nT before calibration to about 1 nT, root mean square error RMSE = 11.7189, indicating that the proposed method has a good temperature calibration effect on the data measured by the fluxgate sensor within the variable temperature background. © Fuji Technology Press Ltd.

关键词： Magnetic field measurement

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Distributed Power Source Planning Based on Improved Particle Swarm Optimization

Distributed Power Source Planning Based on Improved Particle...

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Power and Electrical Technology Conference (APET), Asia

作者： Jiahui Wang Zhili Tan 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

ISBN: (数字)9798350367690

ISBN: (纸本)9798350367706

The multitude of advantages offered by distributed power generation units are recognized as crucial factors in enhancing the security of distribution networks. Ensuring the optimal size and placement of distributed generation (DG) units not only enhances system security but also increases profitability. Haphazard placement of DG units may lead to adverse impacts on the power system, resulting in increased power losses and diminished voltage distribution. Therefore, to ensure the optimal size and placement of DG units, this paper proposes a method to optimize DG scheduling and operation, thereby improving voltage distribution, reducing power losses, and enhancing the overall reliability of the entire grid. To reduce power loss (PL) and improve voltage distribution (VP), an adaptive chaotic particle swarm optimization (ACPSO) algorithm is employed to determine the optimal locations and sizes of DG units. By comparing this algorithm with traditional approaches, its efficacy in enhancing performance can be discerned.

关键词： Profitability Voltage Distributed power generation Power system reliability Planning Security Reliability Particle swarm optimization Optimization Testing

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Semantic Coherence Guided Multiview Similarity for Image Classification with Varied Supervision

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 3072-3082页

作者： Zhang, Chunjie Cao, Ping Bai, Huihui Zheng, Xiaolong Beijing Jiaotong University Institute of Information Science Beijing100044 China Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China University of Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems The State of Key Laboratory of Management and Control for Complex System Institute of Automation Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100190 China

Pairwise similarity has been widely used for image classification by propagating the class information from labeled images to unlabeled images and predicting the classes of unlabeled images accordingly. Although widely used, pairwise similarity based classification methods have two drawbacks. On one hand, visual-semantic similarity consistency does not always hold due to semantic gap. On the other hand, the reliability of pairwise similarity is also influenced by the number and underlying distribution of labeled images. A well-designed classification model cannot be easily generalized to few-shot classification tasks. To solve these two problems, in this article, we propose a semantic coherence guided multiview similarity (SCG-MV) for image classification method with varied supervision levels. We measure the semantic coherence between image pairs for classifier learning. This is achieved by first conducting line search along the line segment between each image pair, and then measure the semantics of each interval. The similarity of one image pair can then be obtained by aggregating the semantic coherence of all the intervals. In this way, we can model image similarities more finely and discriminatively. To make use of multiview information, we also use the semantic coherence guided image similarities to learn multiview classifiers. The proposed semantic coherence guided method can be combined with other similarity based methods for image classification with various levels of supervision. We conduct both few-shot and fully supervised classification experiments on four datasets with the results well prove the discriminative power and generalization ability of the proposed method. © 2020 IEEE.

关键词： Semantics

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Fault-Tolerant Model Predictive Load Frequency control for Power systems with Electrolytic Aluminum Load Under New Event-triggered Mechanism

Fault-Tolerant Model Predictive Load Frequency Control for P...

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Fully Actuated System Theory and Applications (CFASTA), Conference on

作者： Shisen Zhou Ziqian Wang Xiongbo Wan 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

ISBN: (数字)9798350373691

ISBN: (纸本)9798350373707

In this paper, the problem of event-based fault-tolerant model predictive load frequency control is investigated for the power system where the electrolytic aluminum load participates in frequency modulation and actuator faults. A dynamic event-triggered mechanism containing an internal dynamic variable (IDV) and an adjustable variable is designed to reduce the data transmission burden. The fault-tolerant model predictive control (FTMPC) problem is expressed as a "min-max" optimization problem (OP). According to the Lyapunov-like function that depends on the IDV, an auxiliary OP with constrained linear matrix inequalities is constructed. By solving the auxiliary OP, the FTMPC controller gains that ensure the input-to-state stability of the closed-loop system can be obtained. The effectiveness of the proposed algorithm is verified through a simulation example.

关键词： Fault tolerance Actuators Aluminum Fault tolerant systems Power system dynamics Power system stability Predictive models

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Fuel Ratio Prediction Model Based on Attention Mechanism

Fuel Ratio Prediction Model Based on Attention Mechanism

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Asian control Conference

作者： Yihan Li Weihua Cao Wenkai Hu Yan Yuan School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

One of the key concerns in today's steel business is how to effectively save energy and reduce emissions, since the concepts of green environmental preservation, energy conservation, and emission reduction are deeply ingrained in people's hearts. Fuel ratio is a key parameter in blast furnace steelmaking. Identifying the factors that influence the fuel ratio and being able to model and predict it can greatly help in guiding the stable operation of the blast furnace, reducing fuel ratio, saving energy, and cutting emissions. In this paper, the fuel ratio mechanism of blast furnace is analyzed, and the parameters affecting the fuel ratio are initially selected. Subsequently, the parameters with the highest correlation to the fuel ratio are identified using the maximum correlation coefficient method. A CNN-LSTM-Attention-based fuel ratio prediction model was developed, and its accuracy and efficacy were demonstrated through comparison with three other models.

关键词： Attention mechanisms Accuracy Energy conservation Steel industry Predictive models Blast furnaces Data models

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