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IEEE International Conference on Industrial Technology (ICIT)

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

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

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.

关键词： Drilling Support vector machines Employee welfare Chaos Geology Clustering methods Clustering algorithms Benchmark testing Data models Optimization

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Regional Multi-Agent Cooperative Reinforcement Learning for City-Level Traffic Grid Signal control

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IEEE/CAA Journal of Automatica Sinica 2024年第9期11卷 1987-1998页

作者： Yisha Li Ya Zhang Xinde Li Changyin Sun School of Automation Southeast UniversityNanjing 210096 Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of EducationSoutheast UniversityNanjing 210096China

This article studies the effective traffic signal control problem of multiple intersections in a city-level traffic system.A novel regional multi-agent cooperative reinforcement learning algorithm called RegionSTLight is proposed to improve the traffic *** a regional multi-agent Q-learning framework is proposed,which can equivalently decompose the global Q value of the traffic system into the local values of several regions Based on the framework and the idea of human-machine cooperation,a dynamic zoning method is designed to divide the traffic network into several strong-coupled regions according to realtime traffic flow *** order to achieve better cooperation inside each region,a lightweight spatio-temporal fusion feature extraction network is *** experiments in synthetic real-world and city-level scenarios show that the proposed RegionS TLight converges more quickly,is more stable,and obtains better asymptotic performance compared to state-of-theart models.

关键词： Human-machine cooperation mixed domain attention mechanism multi-agent reinforcement learning spatio-temporal feature traffic signal control

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Research on Urban Air Quality Prediction Method Based on the Fusion of Wide-area Spatiotemporal Distribution Data

Research on Urban Air Quality Prediction Method Based on the...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Zixing Tao Weihua Cao Chao Gan School of Future Technology China University of Geosciences (Wuhan) Wuhan China School of Future Technology School of Automation Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China University of Geosciences (Wuhan) Wuhan China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

Air quality data exhibit nonlinearity, sensitivity to environmental factors, and long-term dependencies. Numerous factors influence air quality, making accurate predictions based on a single-dimensional dataset impractical. This study proposes a method for urban air quality prediction that integrates wide-area spatiotemporal data, designed to address the unique characteristics of air quality datasets. First, relevant wide-area spatiotemporal data are selected, and their correlations with air quality are systematically analyzed. Second, a Long Short-Term Memory (LSTM) network-based Transformer model is utilized to capture the long-term dependencies in the air quality sequences. Finally, the model successfully generates hourly multi-step predictions for six air pollutants. The experimental results show that the proposed method outperforms the method that relies on air quality data alone for multi-step air quality prediction.

关键词： Deep learning Sensitivity Atmospheric modeling Time series analysis Predictive models Air quality Transformers Environmental factors Spatiotemporal phenomena Long short term memory

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Environment-Dependent Payoffs in Finite Populations

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Chinese Physics Letters 2015年第2期32卷 160-162页

作者：徐微鸿张艳玲谢广明封雪 Center for Systems and Control State Key Laboratory for Turbulence and Complex SystemsCollege of EngineeringPeking University

In finite population games with weak selection and large population size, when the payoff matrix is constant, the one-third law serves as the condition of a strategy to be advantageous. We generalize the result to the cases of environment-dependent payoff matrices which exhibit the feedback from the environment to the population. Finally, a more general law about cooperation-dominance is obtained.

关键词： Environment-Dependent Payoffs in Finite Populations

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RainGaugeNet: CSI-Based Sub-6 GHz Rainfall Attenuation measurement and Classification for ISAC Applications

arXiv

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arXiv 2025年

作者： Li, Yan Yang, Jie Huang, Yixuan Yang, Tao Wen, Chao-Kai Jin, Shi The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Frontiers Science Center for Mobile Information Communication and Security Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China The National Engineering Research Center of Water Resources Efficient Utilization and Engineering Safety Institute of Water Science and Technology Hohai University Nanjing210098 China The Institute of Communications Engineering National Sun Yat-sen University Kaohsiung80424 Taiwan The National Mobile Communications Research Laboratory Southeast University Frontiers Science Center for Mobile Information Communication and Security Nanjing210096 China

Rainfall impacts daily activities and can lead to severe hazards such as flooding. Traditional rainfall measurement systems often lack granularity or require extensive infrastructure. While the attenuation of electromagnetic waves due to rainfall is well-documented for frequencies above 10 GHz, sub-6 GHz bands are typically assumed to experience negligible effects. However, recent studies suggest measurable attenuation even at these lower frequencies. This study presents the first channel state information (CSI)-based measurement and analysis of rainfall attenuation at 2.8 GHz. The results confirm the presence of rain-induced attenuation at this frequency, although classification remains challenging. The attenuation follows a power-law decay model, with the rate of attenuation decreasing as rainfall intensity increases. Additionally, rainfall onset significantly increases the delay spread. Building on these insights, we propose RainGaugeNet, the first CSI-based rainfall classification model that leverages multipath and temporal features. Using only 20 seconds of CSI data, RainGaugeNet achieved over 90% classification accuracy in line-of-sight scenarios and over 85% in non-line-of-sight scenarios, significantly outperforming state-of-the-art methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Channel state information

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Trajectory Tracking for Directional Drilling in Underground Coal Mine Using Robust Model Predictive control 26

Trajectory Tracking for Directional Drilling in Underground ...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Wu, Jiajun Lu, Chengda Li, Hao Wei, Hongchao Wu, Min School of Future Technology China University of Geosciences Wuhan430074 China 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 Xi'an Research Institute Co. Ltd. China Coal Technology and Engineering Group Xi'an710077 China China Coal Research Institute Beijing100013 China

ISBN: (纸本)9798331521950

This paper studies the problem of trajectory tracking control for directional drilling in underground coal mine by devising a robust model predictive control method. First, a directional drilling trajectory extension model is obtained by the linearization of a drilling tool kinematic model through the Taylor series expansion in the borehole depth domain. Then, in view of the influence of the uncertainty of the drilling tool slope and external interference, a robust model predictive control method is introduced in the form of an optimal control problem. A sufficient condition is obtained to calculate the corresponding controller gain matrix on the premise of satisfying the stability of the closedloop control system and the H∞ performance index. At last, the effectiveness of our method is illustrated through simulation analysis. Compared with model predictive control, robust model predictive control directly incorporates the uncertainty of the drilling tool slope and external interference into the control design, enabling it to better adapt to the complex and variable underground coal mine environment. © 2025 IEEE.

关键词： Predictive control systems

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Voltage-controlled robust topological states in perforated beams

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engineering Structures 2025年 338卷

作者： Hong, Fang Zhang, Kai Yao, Yuxin Tang, Lihua Deng, Zichen School of Mechanics Civil Engineering and Architecture Northwestern Polytechnical University Xi'an 710072 China MIIT Key Laboratory of Dynamics and Control of Complex Systems Northwestern Polytechnical University Xi'an 710072 China Department of Mechanical and Mechatronics Engineering University of Auckland 20 Symonds Street Auckland 1010 New Zealand

Topological metamaterials possess waveguiding and energy concentration properties even under perturbations such as defects or disorders. Tunable topological materials offer enhanced flexibility to these properties. In this study, a voltage-controlled strategy is presented to realize the tunable robust topological states. First, a perforated beam with piezoelectric patches and additional steel columns is proposed to analyze the topological properties, and the corresponding numerical model based on Timoshenko beam theory is introduced to analyze the band gap properties and transmission spectra by spectral element method (SEM). Then the effects of the applied voltage on the resonance frequency and equivalent stiffness of piezoelectric patches are characterized. Two new band gaps emerge around the locally resonant band gap and topological phase transition can be achieved by altering the collective displacement of the perforations and resonators. Subsequently, the finite element simulations are presented to validate the accuracy of SEM results, and the equivalent voltage-controlled stiffness is used in SEM to simplify the relationship between voltage and topological properties. The Zak phase calculations demonstrate the topological properties of opened band gaps and further indicate the existence of topologically protected interface states (TPISs). Finally, the transmission spectra are used to analyze the characteristics of TPISs, and the robustness of TPISs is examined by introducing different perturbation factors. The results show that the frequency of topological states can be simply tuned by varying the voltage on the piezoelectric patch, and the topological rainbow trapping can be realized in a designed gradient system without changing the geometric configuration. © 2025 Elsevier Ltd

关键词： Spectral element method Topological metamaterials Topological rainbow trapping Tunable topological states Voltage-controlled stiffness

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Method of Eliminating Velocity Ripples in Brushless DC Motor Based on Internal Model Principle and Sliding Model control 28

Method of Eliminating Velocity Ripples in Brushless DC Motor...

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第28届中国控制与决策会议

作者： He Shuo-yan Li Shi-hua Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of EducationSoutheast University

ISBN: (纸本)9781467397155

In the practical application,Brushless DC motor(BLDCM) faces various disturbances including parametric uncertainties,load disturbance and unmodeled *** disturbances can be divided into two types of periodic disturbances(sinusoidal/cosinoidal) and aperiodic(slowly-varying or constant) *** ripples can be seen as the result of a plurality of periodic interference signals of different frequencies acting on the *** the paper we propose a composite control scheme combining integral sliding mode control(ISMC) based on disturbance observer(DO) embedded with the internal model principle to improve the speed performance of *** disturbance observer,such as extended state observer(ESO) can only asymptotically estimate slowly-varying or constant disturbances and is not good at estimating periodic *** we combine internal model of disturbance into disturbance observe for high ***,the estimates are introduced in the feedforward compensation,and a composite speed controller is *** last,experimental comparisons with conventional proportional-integral(PI) and ISMC+ESO,are given to validate the effectiveness of the proposed method.

关键词： Brushless DC Motor Internal model principle Steady-state velocity ripple elimination ESO Integral sliding mode control(ISMC) Disturbance observer

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A Dynamic Colored Traveling Salesman Problem and Its Solution 36

A Dynamic Colored Traveling Salesman Problem and Its Solutio...

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第36届中国控制会议

作者： Xianghu Meng Jun Li Xianzhong Dai Ministry of Education Key Laboratory of Measurement and Control of Complex Engineering Systems Southeast University

ISBN: (纸本)9781538629185

The colored traveling salesman problem(CTSP) is a generalization of the well-known multiple traveling salesman problem(MTSP). In CTSP, each city has one to multiple colors, allowing a salesman in the same color to visit exactly once. This work presents a dynamic CTSP(DCTSP) in which the weights of edges among the cities change over time. To solve the DCTSP, we propose a variable neighborhood search(VNS) algorithm with a direct-route encoding and random initialization. The VNS is initialized by greedy operations at two stages and an appropriate population immigrant scheme is used in it to perform the population search in the dynamic environment. Extensive experiments are conducted to test the effectiveness of the greedy initialization and the immigrant scheme with the best population. The results show that the enhanced VNS can track the environment changes of DCTSP more rapidly and effectively.

关键词： Dynamic colored traveling salesman problem Variable neighborhood search Dynamic optimization problem

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Short-term wind power forecast with turning weather based on DBSCAN-RFE-LightGBM

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Renewable Energy 2025年 251卷

作者： Wu, Zhenlong Fan, Xinyu Bian, Guibin Liu, Yanhong Zhang, Xiaoke Chen, YangQuan School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China State Key Laboratory of Intelligent Agricultural Power Equipment Zhengzhou University Luoyang471000 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Electric Power Research Institute of State Grid Henan Electric Power Company Zhengzhou450052 China Lab Department of Mechanical Engineering School of Engineering University of California MercedCA95343 United States

At present, the wind power prediction technology is becoming more and more mature, however, the turning weather brings a decrease in prediction accuracy due to its suddenness and instability. In this paper, a wind power prediction method considering the turning period and non-turning period respectively is proposed to weaken the adverse effects caused by turning weather. Firstly, the prediction effects of multiple models are compared, density-based spatial clustering of applications with noise (DBSCAN) is selected as the outlier processing method, recursive feature elimination (RFE) is used as the feature selection method, and Light gradient boosting machine (LightGBM) is used for prediction. The combined prediction method based on DBSCAN-RFE-LightGBM can reduce the influence of abnormal data and redundant features and improve the prediction effect. Then, the sliding window is set to detect the turning period. Considering that the turning weather is an emergency and does not happen frequently, the amount of data is small, which leads to the inability to train the model well. Generative adversarial networks (GAN) are applied to expand the turning period data. The LightGBM is trained and predicted by using the expended turning period data. Finally, the time-division prediction results are merged. Using the data collected from wind farms for short-term power prediction experiments, the time-segment prediction method proposed in this paper with GAN reduces MAE by 1.913 and RMSE by 3.351 on a single unit compared with the non-differentiated period. © 2025 Elsevier Ltd

关键词： Merging

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