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Asian control Conference

作者： Jinyu Li Hongchao Wei Chengda Lu Wangnian Li Quanxin Li Min Wu 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 Xi'an Research Institute Co. Ltd. China Coal Technology and Engineering Group Xi'an China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

In this paper, the effect of the initial state of the drilling system on trajectory tracking is taken into account in the directional drilling trajectory tracking control. Firstly, the trajectory model describing the two-dimensional drilling trajectory of the rotary steering system (RSS) is introduced. To improve the accuracy of trajectory tracking control and reduce the trajectory deviation, a fixed-time sliding mode control (FTSMC) method is introduced. Then, the stability of the closed-loop system is analyzed, and the range of controller parameters satisfying the stability condition is obtained. Finally, the effectiveness of the designed control strategy is verified by simulation experiments.

关键词： Drilling Upper bound Trajectory tracking Steering systems Stability analysis Trajectory Closed loop systems

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Industrial Data Extraction Method Based on Correlation Matrix and Multidimensional Probability Distribution 6

Industrial Data Extraction Method Based on Correlation Matri...

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6th International Conference on Power and Renewable Energy, ICPRE 2021

作者： Peng, Su Yue, Sun Zhihong, Luo China Ship Development and Design Center Wuhan China Harbin Engineering University College of Intelligent Systems Science and Engineering Harbin China North China Electric Power University School of Control and Computer Engineering Beijing China

ISBN: (纸本)9781665449618

The operation data of power plant has the characteristics of multivariable and big data, which is widely used in the research of data-driven modeling. Effective data extraction is an important part of data-driven modeling, and the performance of data extraction algorithm directly affects the accuracy of modeling. In this paper, an effective data extraction method based on correlation matrix and vine structure is proposed. The rank correlation coefficient is used to measure the correlation between variables and construct the correlation matrix. According to the correlation matrix, important variables are selected and grouped. Vine structure is used to establish a multi-dimensional joint distribution model for each group of variables. The data with high probability are extracted by confidence interval as effective data. Finally, the extracted results of multiple combinations are combined to form an effective data set. The SCR data are used for example verification, and the results show that the proposed method can extract effective data and improve the correlation between variables significantly. © 2021 IEEE.

关键词： Data mining

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Prediction model of photovoltaic output power based on VMD-EMD-BiLSTM

Prediction model of photovoltaic output power based on VMD-E...

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Industrial Cyber-Physical systems (ICPS)

作者： Shaolong Zheng Danyun 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 Wuhan China

The accuracy of photovoltaic (PV) power prediction is significantly influenced by the high complexity and volatility of the PV sequence. The existing methods for predicting photoelectric power are difficult to effectively mine and analyze the internal variation law of data. To improve the accuracy of PV power prediction, a new method is proposed that first performs variational mode decomposition (VMD) and empirical mode decomposition (EMD), and then establishes a bi-directional long and short-term memory neural network (BiLSTM) for PV output power prediction. The proposed method extracts the amplitude and frequency characteristics of the PV output power series through VMD. After that, the residual term with strong non-stationarity is generated, which still has more sequence characteristics. The residual term is then decomposed by EMD for the second time to extract more features. Finally, the BiLSTM model is established to conduct bidirectional mining for PV power data and predict PV output power. The actual PV data is used to test the experimental results, which show that the proposed VMD-EMD-BiLSTM prediction model has better prediction performance.

关键词： Photovoltaic systems Empirical mode decomposition Simulation Neural networks Predictive models Cyber-physical systems Prediction algorithms

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Human pose recognition based on multiple features and random forest algorithm 2

Human pose recognition based on multiple features and random...

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2nd International Conference on Computer Vision, Image, and Deep Learning

作者： Fang, Di Wang, Hong Meng, Fanglong 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

ISBN: (数字)9781510646827

ISBN: (纸本)9781510646810

Human pose recognition based on bone node data collected by depth camera is a key problem in the field of human-computer interaction. To improve the accuracy of human pose recognition, a new algorithm based on multiple features and random forest model is proposed. Firstly, a 93-dimensional vector is defined, which contains the coordinate feature of the joint and the distance feature, and the distance feature is selected according to the spatial position of the joint. Then, in the process of body pose recognition, the random forest model is combined with Bagging algorithm to ensure the balance of samples, so as to improve the classification performance of the classifier for different samples. Finally, the performance test of the constructed classifier is carried out on the UTKinect-action3D Dataset. The experimental result shows that the algorithm can effectively identify a variety of human posture, and the recognition rate reaches more than 90%. The fusion of multiple features is of great significance to improve the accuracy of human posture recognition. © 2021 SPIE.

关键词： Decision trees

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Hybrid event-and time-triggered control for double-integrator heterogeneous networks

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Science China(Information Sciences) 2019年第2期62卷 73-84页

作者： Gaopeng DUAN Feng XIAO Long WANG Center for Systems and Control College of Engineering Peking University Research Institute of Intelligent Control and Systems Harbin Institute of Technology

This paper investigates the state consensus for double-integrator networks under heterogeneous interaction topologies. For double-integrator networks, the setting of heterogeneous topologies means that position and velocity information flows are modeled by two different graphs. The corresponding protocol proposed in this paper is based on edge-event-triggered control. The events based on position information are irrelevant to velocity information and independent of the events based on velocity information. And for different edges, the corresponding events are activated independently of each other. Once an event occurs,the agents connected by the associated edge will sample their corresponding relative state information and update their corresponding controllers. Furthermore, under the presented event-triggering rules, the state consensus of double-integrator networks can be achieved by designing appropriate parameters. In addition,the proposed protocol with the event-triggering rules can effectively improve the system performance and avoid the occurrence of Zeno behaviors. Finally, a simulation example is worked out to verify the theoretical analysis.

关键词： consensus sampled-data control multi-agent systems edge-event-triggered control heterogeneous networks

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Optimized Task Assignment and Predictive Maintenance for Industrial Machines using Markov Decision Process

arXiv

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arXiv 2024年

作者： Nasir, Ali Mekid, Samir Sawlan, Zaid Alsawafy, Omar Control and Instrumentation Engineering Department KFUPM Dhahran Saudi Arabia Mechanical Engineering Department KFUPM Dhahran Saudi Arabia Mathematics Department KFUPM Dhahran Saudi Arabia Systems Engineering Department Dhahran Saudi Arabia Interdisciplinary Research Center for Intelligent Manufacturing and Robotics KFUPM Dhahran Saudi Arabia

This paper considers a distributed decision-making approach for manufacturing task assignment and condition-based machine health maintenance. Our approach considers information sharing between the task assignment and health management decision-making agents. We propose the design of the decision-making agents based on Markov decision processes. The key advantage of using a Markov decision process-based approach is the incorporation of uncertainty involved in the decision-making process. The paper provides detailed mathematical models along with the associated practical execution strategy. In order to demonstrate the effectiveness and practical applicability of our proposed approach, we have included a detailed numerical case study that is based on open source milling machine tool degradation data. Our case study indicates that the proposed approach offers flexibility in terms of the selection of cost parameters and it allows for offline computation and analysis of the decision-making policy. These features create and opportunity for the future work on learning of the cost parameters associated with our proposed model using artificial intelligence. © 2024, CC BY.

关键词： Optimization

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Robust Indoor Localization and Map Matching Algorithm with Visual SLAM

Robust Indoor Localization and Map Matching Algorithm with V...

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Chinese Automation Congress (CAC)

作者： Zhebin Wang Zhenfeng Xue Siyi Xie Xiongtao Zhang Yong Liu School of Information Engineering Huzhou University Huzhou China Intelligent Perception and Control Center Huzhou Institute of Zhejiang University Huzhou China Institute of Cyber-Systems and Control Zhejiang University Hangzhou China College of Mechanical and Automotive Engineering Xiamen University of Technology Xiamen China

With the rapid development of intelligent robotic technology, global positioning and localization algorithm is attracting much more attention in recent years, especially for unknown indoor environments. In such case, global positioning system (GPS) will lose its effect, and relative positioning systems become more important, including UWB, IMU and VIO, which exist drift phenomenon with time. In this paper, we propose a novel global localization and map matching algorithm suitable for indoor and outdoor unseen environments. Its core algorithm is based on visual SLAM to generate the sparse map point cloud as well as historical trajectory path. After simply annotating the site drawings, a two-dimensional semantic map of the environment can be obtained. Then the point cloud and trajectory path are matched with the semantic map through a global search algorithm. Finally, the global localization in unknown environment can be easily achieved and the historical trajectory path can be displayed accurately on the site drawing. Real-world experiment shows the effectiveness and robustness of the method.

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Distributed formation control of networked mechanical systems

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IFAC-PapersOnLine 2022年第13期55卷 294-299页

作者： N. Javanmardi P. Borja M.J. Yazdanpanah J.M.A. Scherpen Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Cognitive Robotics Delft University of Technology Delft The Netherlands Jan C. Wilems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen Groningen The Netherlands

This paper investigates a distributed formation tracking control law for large-scale networks of mechanical systems. In particular, the formation network is represented by a directed communication graph with leaders and followers, where each agent is described as a port-Hamiltonian system with a constant mass matrix. Moreover, we adopt a distributed parameter approach to prove the scalable asymptotic stability of the network formation, i.e., the scalability with respect to the network size and the specific formation preservation. A simulation case illustrates the effectiveness of the proposed control approach.

关键词： Autonomous systems Cooperative control Networked control systems Large-scale systems Port-Hamiltonian systems Scalability

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Semi-supervised Anomaly Detection and Location Based on Generative Adversarial Network 6

Semi-supervised Anomaly Detection and Location Based on Gene...

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6th International Conference on Signal and Image Processing, ICSIP 2021

作者： Liu, Wei Gao, Mingqiang Duan, Shuaidong Wei, Longsheng China University of Geosciences School of Automation 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: (纸本)9780738133737

With the development of artificial intelligence, the anomaly detection plays more and more important role in security monitoring field. Because it is difficult to label abnormal data, most of the supervised methods consumed a lot of manpower and obtained low performance and generality. Inspired by this motivation, this paper proposes a semi-supervised method for anomaly detection in video frames based on GAN (Generative Adversarial Network), in which only normal data was used as the training sample. The quality gap between the predicted frame and the ground truth is used as the basis to determine whether it is abnormal. Moreover, the mathematical morphology approach was adopted to locate the anomaly area in the frames. Experiments show that our method can successfully detect abnormal frames in video and can also locate the area where abnormal behavior occurs in frames. © 2021 IEEE.

关键词： Generative adversarial networks

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A Novel Rate of Penetration Fusion Modeling Method based on Improved Dung Beetle Optimization Algorithm and Support Vector Regression 26

A Novel Rate of Penetration Fusion Modeling Method based on ...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Zhou, Yang Zhang, Zihan Chen, Shipeng Wu, Min Chen, Xin 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 School of Future Technology China University of Geosciences Wuhan430074 China

ISBN: (纸本)9798331521950

The rate of penetration (ROP) is a critical indi-cator for evaluating drilling efficiency. Developing an accurate ROP model is essential for optimizing drilling performance and addressing process control challenges. However, ROP modeling in deep geological drilling is complicated by nonlinearity, diverse working conditions, and high-dimensional variations. To overcome these challenges, a fusion modeling approach for ROP is proposed. First, the fuzzy C-means clustering method is applied to classify drilling data into different working con-ditions. Based on this classification, support vector regression is employed to develop ROP sub-models, effectively addressing nonlinearity. To further enhance model accuracy, an improved dung beetle optimization algorithm (IDBO) is designed to deter-mine optimal model parameters and integrate the sub-models, thereby resolving issues related to multiple working conditions and high-dimensional variations. The IDBO incorporates four key enhancements, average weight, chaos disturbance, modified local search, and re-updating of the best solution, to strength-en its global search capability. Comparative results using the IEEE CEC2017 benchmark test functions demonstrate that the proposed algorithm outperforms others in 12 test functions, highlighting its strong global optimization ability. Additionally, results from real-world drilling data validate the effectiveness of the proposed modeling approach in practical applications. © 2025 IEEE.

关键词： Clustering algorithms

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