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第41届中国控制会议

作者： Mingxiang Liao Yongbo Li Guangyao Wang Zhenyu Lei Zihao Zhang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems System Integration and Industrial Robotics Laboratory

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Ultra-wideband（UWB） positioning technology has absolute advantages due to its centimeter-level accurate positioning ***,the positioning accuracy is greatly reduced because of the inability to completely solve the error caused by non-line-of-sight（NLOS） and multi-path *** present,the Chan-Taylor（C-T） combination algorithm proposed by scholars can achieve a good positioning effect in the line-of-sight（LOS） environment,but in the NLOS environment,positioning deviation of this algorithm is large .Aiming at the problem of low localization accuracy in NLOS environment,this paper presents a Chan-Taylor-Kalman（CTK） joint positioning algorithm based on UWB and adapted to NLOS *** basic idea of CTK joint positioning algorithm is to introduce weight coefficients on the basis of C-T combination algorithm to reduce the situation of non-convergence of positioning results;then combined with the improved Kalman filter to eliminate outliers and eliminate the influence of the non-line-of-sight *** show that under the same NLOS environment,the probability of the positioning error of the C-T combination positioning algorithm greater than 20 cm is about 60%,while the probability of the positioning error of the CTK joint positioning algorithm greater than 10 cm is only about 10%.

关键词： UWB NLOS indoor positioning Chan-Taylor combination algorithm Kalman filter

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A LESO Based Backstepping controller Considering Coal Seam Hardness for Rotary Speed in Coal Mine Tunnel Drilling Process

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Journal of systems Science & complexity 2024年第5期37卷 1789-1808页

作者： CHEN Luefeng LIU Xiao WU Min LU Chengda PEDRYCZ Witold HIROTA Kaoru School of Automation China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan 430074China Department of Electrical and Computer Engineering University of AlbertaEdmontonAB T6R2G7Canada The Tokyo Institute of Technology Yokohama 226-8502Japan

In the process of coal mine drilling,controlling the rotary speed is important as it determines the efficiency and safety of *** this paper,a linear extended state observer(LESO)based backstepping controller for rotary speed is proposed,which can overcome the impact of changes in coal seam hardness on rotary ***,the influence of coal seam hardness on the drilling rig’s rotary system is considered for the first time,which is reflected in the numerical variation of load torque,and a dynamic model for the design of rotary speed controller is *** an LESO is designed to observe the load torque,and feedforward compensation is carried out to overcome the influence of coal seam *** on the model of the compensated system,a backstepping method is used to design a controller to achieve tracking control of the rotary ***,the effectiveness of the controller designed in this paper is demonstrated through simulation and field experiments,the steady-state error of the rotary speed in field is 1 r/min,and the overshoot is reduced to 5.8%.This greatly improves the stability and security,which is exactly what the drilling process requires.

关键词： Backstepping extended state observer hydraulic tunnel drilling machine rotary speed

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Graph-based Selection-Activation Reinforcement Learning for Heterogenous Multi-agent Collaboration 43

Graph-based Selection-Activation Reinforcement Learning for ...

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43rd Chinese control Conference, CCC 2024

作者： Chen, Hao-Xiang Zhang, Xi-Wen Shen, Jun-Nan Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex System School of Automation Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401135 China

ISBN: (纸本)9789887581581

In practical applications of multi-agent systems, agents are often heterogeneous, and each type of them typically has different task objectives. For heterogeneous multi-agent reinforcement learning (HMARL), the diversity of agent types and the unbalanced agent number of each type can lead to the curse of dimensionality and non-stationary. Moreover, the increase in the number of heterogeneous agents may result in slow convergence during training. This paper proposes a graph-based selection-activation reinforcement learning (GSARL) method for training heterogenous multi-agent collaboration strategies. It first constructs agents based on their types, then extracts the global adjacency matrices and the ally adjacency matrices from the agents' observations, and calculates the global feature matrices. Afterwards, GSARL utilizes hierarchical graph convolutional network to sequentially convolve the global information and ally information, obtaining action logits based on agent types. By using the neural topology graph and the selection-activation method, the optimal multi-agent collaboration configuration is obtained through combinatorial optimization. Experiments are conducted in an adversarial combat simulation environment involving collaborative Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs). Simulation results show that the proposed method can accelerate convergence while allowing that each type of heterogeneous agents can leverage its unique advantages. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Unmanned aerial vehicles (UAV)

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From Unmanned systems to Autonomous intelligent systems

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Engineering 2022年第5期8卷 16-19页

作者： Jie Chen Jian Sun Gang Wang Department of Control Science and Engineering Tongji UniversityShanghai 201804China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai 200092China Key Lab of Intelligent Control and Decision of Complex Systems Beijing Institute of TechnologyBeijing 100081China Beijing Institute of Technology Chongqing Innovation Center Chongqing 401120China

*** Artificial intelligence(AI)is a rapidly growing field of technol-ogy,which“will enliven inert objects,much as electricity did more than a century *** that we formerly electrified will now cognitize”[1].AI advances are constantly pushing the frontier of what machines can *** attention is being placed on AI research,as well as its development and deployment by commer-cial investors,defense strategists,and policy makers[2].

关键词： frontier inert policy

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Pattern Matching of Industrial Alarm Floods Using Word Embedding and Dynamic Time Warping

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 1096-1098页

作者： Wenkai Hu Xiangxiang Zhang Jiandong Wang Guang Yang Yuxin Cai School of Automation China University of GeosciencesWuhan 430074Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex SystemsWuhan 430074 Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan 430074China College of Electrical Engineering and Automation Shandong University of Science and TechnologyQingdao 266590China

Dear Editor,This letter proposes a new pattern matching method based on word embedding and dynamic time warping(DTW)to identify groups of similar alarm ***,alarm messages are transformed into numeric values that represent alarms and also reflect the relationships between alarm ***,similarities between numerically encoded alarm flood sequences are calculated by DTW and groups of similar floods are identified via *** effectiveness of the proposed method is demonstrated by a case study with alarm&event data obtained from a public industrial simulation model.

关键词： Word Alarm DTW

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A Novel Thermal Analysis Method for Tubular PM Linear Motors Based on Transfer Learning

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 7379-7388页

作者： Wu, Tao Dai, Peipei Xue, Gang Guo, Youguang Lei, Gang Zhu, Jianguo Wang, Yifei China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China University of Technology Sydney School of Electrical and Data Engineering NSW2007 Australia University of Sydney School of Electrical and Computer Engineering SydneyNSW2006 Australia

Thermal modeling and analysis are critical for permanent magnet linear synchronous motors (PMLSMs), particularly in multi-physical analysis and motor design optimization. This paper proposes a new method for thermal steady state modeling and analysis based on transfer learning, which combines the thermal mechanism model with the data-driven model to achieve high-precision temperature analysis with small number of finite element analysis (FEA) samples. First, an equivalent thermal circuit (ETC) model is established for PMLSMs based on the structural parameters and heat transfer principles. Second, a hybrid model based on transfer learning is developed: the model is pre-trained by a large sample set generated from the ETC in the source field (mechanism analytical model), and then the trained model is transferred to the target field (FEA data-driven model). The parameters of the neural network in the target field are optimized by a few FEA sample sets with higher precision to improve the prediction accuracy of the target model. Experimental results demonstrate that the proposed method significantly boosts the performance of the deep neural network (DNN) model, particularly when training sets are small. The root mean square error (RMSE) of the DNN model decreases by 0.901 in the 5:5 (training/testing) dataset and 1.726 in the 2:8 dataset;and the corresponding R2 and maximal absolute error of motor performances are significantly decreased. The smaller the proportion of the training set, the better performance of the hybrid modeling will achieve. Furthermore, a temperature rise experiment conducted on a prototype motor validates the efficacy and precision of the proposed hybrid modeling methodology. © 2015 IEEE.

关键词： Permanent magnets

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Compound Fault Separation and Diagnosis Method Based on FSA-CNN and DAN

Compound Fault Separation and Diagnosis Method Based on FSA-...

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2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes, SAFEPROCESS 2021

作者： Dong, Jiechao Yan, Liping Xia, Yuanqing Beijing Institute of Technology Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing China

ISBN: (纸本)9781665401159

With the industry becoming larger and more complicated, the technology of fault diagnosis becomes more and more important. Due to the strong coupling and non-stationarity of the compound fault, the existing fault diagnosis methods cannot accurately identify all single faults' detailed information contained in the compound fault. This paper proposes a compound fault separation and diagnosis method based on FSACNN and DAN. Firstly, in order to highlight certain frequency segments, frequency segment attention module is added to CNN. Secondly, a compound fault feature separation framework based on DAN is proposed, which can separate compound fault to two fault components accurately. Thiredly, a signature matrix is introduced into ELM to improve the performance of the classifier. Finally, ablation experiments are designed to prove the advantage of the proposed method. © 2021 IEEE.

关键词： Failure analysis

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Stability Analysis of Event-Triggered Networked control systems with Time-Varying Delay and Packet Loss

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系统科学与复杂性学报（英文版） 2021年第1期34卷 265-280页

作者： WANG Zhao SUN Jian BAI Yongqiang The Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of TechnologyBeijing 100081China The Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of TechnologyBeijing 100081China The Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of TechnologyBeijing 100081China

Networked control systems(NCSs)are facing a great challenge from the limitation of network communication ***-triggered control(ETC)is often used to reduce the amount of communication while still keeping a satisfactory performance of the system,by transmitting the measurements only when an event-triggered condition is ***,some network-induced problems would happen inevitably,such as communication delay and packet loss,which can degrade the control performance significantly and can even lead to *** this paper,a periodic event-triggered NCS considering both time-varying delay and packet loss is *** system is discretized into a piecewise linear system with *** the model is handled by a polytopic over-approximation method to be more suitable for stability ***,stability conditions are obtained and presented in terms of linear matrix inequalities(LMIs).The result is illustrated by a numerical example.

关键词： Delay event-triggered control networked control systems packet loss stability analysis

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Multi-Level Features Fusion for Zero-Shot Object Pose Estimation 26

Multi-Level Features Fusion for Zero-Shot Object Pose Estima...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Huang, Nanxin Xu, Chi 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 Wuhan China

ISBN: (纸本)9798331521950

Driven by advancements in industrial production and artificial intelligence, the need for pose estimation of new ob-jects in areas like robotic manipulation and virtual reality is increasing. We introduce a zero-shot object pose estimation approach that identifies the poses of objects excluded from the training dataset, removing the requirement for re-modeling. The method is built around a multi-level features fusion framework de-signed to enhance generalization. First, a trainable feature extraction module filters and selects multi-level features extracted by the backbone network. Unlike traditional convolutional ker-nels, we incorporate a dynamic convolution kernel to enhance the feature extraction capability. Second, in the feature fusion module, we adopt a dynamic weight generation strategy to perform weighted fusion of multi-level features. This method enhances template matching by effectively describing similarities between unseen objects (those absent from the training set) and templates, leveraging robust and adaptive feature representations to narrow the gap with seen objects. Experimental results demonstrate that our approach achieves state-of-the-art performance on two popu-lar benchmark datasets, LineMod and LineMod-Occlusion, proves that our method has better generalization than previous models. © 2025 IEEE.

关键词： Industrial robots

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Parallel Learning:Overview and Perspective for Computational Learning Across Syn2Real and Sim2Real

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IEEE/CAA Journal of Automatica Sinica 2023年第3期10卷 603-631页

作者： Qinghai Miao Yisheng Lv Min Huang Xiao Wang Fei-Yue Wang School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China School of Artificial Intelligence Anhui UniversityHefei 266114China Qingdao Academy of Intelligent Industries Qingdao 230031China State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China IEEE

The virtual-to-real paradigm,i.e.,training models on virtual data and then applying them to solve real-world problems,has attracted more and more attention from various domains by successfully alleviating the data shortage problem in machine *** summarize the advances in recent years,this survey comprehensively reviews the literature,from the viewport of parallel ***,an extended parallel learning framework is proposed to cover main domains including computer vision,natural language processing,robotics,and autonomous ***,a multi-dimensional taxonomy is designed to organize the literature in a hierarchical ***,the related virtual-toreal works are analyzed and compared according to the three principles of parallel learning known as description,prediction,and prescription,which cover the methods for constructing virtual worlds,generating labeled data,domain transferring,model training and testing,as well as optimizing the strategies to guide the task-oriented data generator for better learning *** issues remained in virtual-to-real are ***,the future research directions from the viewpoint of parallel learning are suggested.

关键词： Machine learning parallel learning parallel systems sim-to-real syn-to-real virtual-to-real

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