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A Weighting Factors Autotuning Strategy for Model Predictive Torque control of Permanent Magnet Synchronous Motor Based on Adaptive Multi-Objective Black Hole Algorithm 42

A Weighting Factors Autotuning Strategy for Model Predictive...

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42nd Chinese control Conference, CCC 2023

作者： Zhou, Zixiang Wu, Tao School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China System Integration and Industrial Robotics Laboratory Wuhan430074 China

ISBN: (纸本)9789887581543

The rational design of weighting factors in the cost function for finite control set model predictive torque control (FCS-MPTC) has been a matter of great interest in power electronics and electrical drives. In order to solve this problem, a weighting factors autotuning strategy for FCS-MPTC of permanent magnet synchronous motor (PMSM) based on the adaptive multi-objective black hole algorithm (AMOBH) is proposed. In this paper, the design process of the FCS-MPTC algorithm is first analyzed in detail. Then, an AMOBH algorithm that can take into account both population convergence and population diversity is introduced, and based on this algorithm, the design problem of the weighting factors is successfully transformed into a multi-objective optimization problem by means of reconstructing the cost function and designing the motor operation information collected in real time as the objective functions of the multi-objective optimization algorithm. Simulation results show that the proposed method can find a set of weighting factor combinations suitable for different working condition requirements, and these weighting factors can effectively improve the operation performance of the PMSM system. © 2023 Technical Committee on control Theory, Chinese Association of automation.

关键词： Cost functions

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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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Identification and Prediction of Microvascular Invasion in Hepatocellular Carcinoma Based on Multiple Sequence Fusion Images 43

Identification and Prediction of Microvascular Invasion in H...

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43rd Chinese control Conference, CCC 2024

作者： Lu, Mingguang Zhu, Yuan China University of Geosciences School of Automation 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 Wuhan430074 China

ISBN: (纸本)9789887581581

Hepatocellular carcinoma (HCC) is one of the most difficult diseases to be treated fundamentally in the medical field in today's world, and the recurrence rate of HCC has been increasing year by year in recent years. The study on the identification and prediction of microvascular invasion phenomenon (MVI) in hepatocellular carcinoma is beneficial to the postoperative prevention of hepatocellular carcinoma patients, which can greatly improve the survival probability of patients after surgery. In this study, a new MVI prediction method (MSF-GWO-GPR) was proposed, which combines the multi-sequence image features of imaging histology, sifts out the unimportant features through the T-test method and the dimensionality reduction of Lasso regression, and establishes the multi-sequence Gaussian process regression (GPR) prediction model for data fusion, and combined with the Gray Wolf Optimization (GWO) algorithm to design a suitable optimization objective function to optimize the hyper-parameters in the GPR, in order to get better prediction results. Compared with the existing methods, the data from different sequences of MRI images are selected for fusion in this study, which makes the feature data richer and more complete, and the model applicability is better;moreover, by optimizing the hyperparameters in the model through the GWO, the model can be made more accurate, reduce the risk of misidentified prediction, and improve the accuracy rate. The experimental results show that MSF-GWO-GPR outperforms widely used machine learning methods in terms of accuracy, F1 score, precision, recall and AUC metrics. This may provide a new idea for the identification and prediction of MVI in hepatocellular carcinoma. © 2024 Technical Committee on control Theory, Chinese Association of automation.

关键词： Dimensionality reduction

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Kalman Filter Adaptive Speed Tracking control for High-speed Trains with Noise Interference 7

Kalman Filter Adaptive Speed Tracking Control for High-speed...

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7th International Conference on intelligent Robotics and control Engineering, IRCE 2024

作者： Zhang, Chunli Xie, Yuwei Yan, Lei Qian, Fucai Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing College of Automation and Information Engineering Xi'an China

ISBN: (纸本)9798350352399

In this paper, high-speed train is regarded as a single prime point, a new tracking control method combining adaptive control and Kalman filtering is proposed. This paper solves the noise interference problem in the tracking control process of high-speed train. The method first linearises the original system, then uses Kalman filtering to do model estimation, and the controller is designed for the predictive model through adaptive control. Finally, MATLAB is used to simulate and verify the effectiveness of this method. This proposed method successfully solves the problems of nonlinearity, noise interference and unknown parameters in system. The error caused by resistance in the high-speed train tracking control process can be dealt with effectively. © 2024 IEEE.

关键词： Kalman filters

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Pipeline Anomaly Detection Based on KPCA and Cosine Distance Prototypical Network 26

Pipeline Anomaly Detection Based on KPCA and Cosine Distance...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Yi, Kang Liu, Xin Hu, Wenkai Li, Yupeng 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

ISBN: (纸本)9798331521950

Accurately and promptly detecting the pipeline anomaly is crucial to the safe operation of pipeline systems, while a difficulty lies in that many existing methods require massive data for training models. However, pipelines are running under normal state for the most of the time, and labeled pipeline anomaly data is usually scarce. Among the commonly used sensors, vibration sensors are widely utilized in pipeline detection because of their advantages such as easy installation and high sensitivity. However, the vibration signal shows non-stationary characteristics when anomalies occur, and are contaminated by noises, making it difficult to represent the actual state with features extracted from either the time or frequency domain. Accordingly, this paper proposes a pipeline anomaly detection method based on the KPCA (kernel principal component analysis) and cosine distance prototypical network. First, features are extracted from original signals;then, the feature dimension is reduced by KPCA;last, the cosine distance is introduced to the prototypical network for anomaly detection. The effectiveness of the proposed method is demonstrated by case studies involving experimental data. © 2025 IEEE.

关键词： Frequency domain analysis

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Bearing Fault Diagnosis Under Variable Working Conditions Based on Domain Adaptation and Improved Depthwise Separable Convolution Network 26

Bearing Fault Diagnosis Under Variable Working Conditions Ba...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Kuang, Zehui Mao, Fan Zhao, Xingyu Wan, Xiongbo 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 School of Future Technology 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

Domain adaptation methods and appropriate feature extractors are usually applied to solve the problem that the variable working conditions of bearings affect the effectiveness of the fault diagnostic framework. However, the existing feature extractors are too complex to be deployed in real industries. In this paper, we propose a lightweight framework called improved depthwise separable convolution network (IDSCN) for the diagnosis of bearing faults in variable operating conditions. IDSCN is built on wavelet convolution and depthwise separable residual convolution (DSRC), where the wavelet convolution is used to extract frequency features from the source and target domains, and DSRC guarantees the performance of feature extraction with reduced parameters and computation. To minimise the distribution difference between the source and target domains, the local maximum mean difference is incorporated into the proposed framework. The performance of the proposed framework is validated on the Paderborn University dataset, involving a total of 6 transfer tasks. The experiment shows that the proposed framework achieves satisfactory results and outperforms other frameworks in bearing fault diagnosis. © 2025 IEEE.

关键词： Bearing fault diagnosis Depthwise separable residual convolution Domain adaptation Wavelet convolution

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Tracking performance limitations of networked control systems with packet dropouts and logarithmic quantization 41

Tracking performance limitations of networked control system...

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第41届中国控制会议

作者： Jianhao Li Xiaowei Jiang Hao Chen Shaoying Wang Xianhe Zhang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Fujian Provincial Key Laboratory of Intelligent Identification and Control of Complex Dynamic System College of Science Binzhou University School of Electrical Engineering and Automation Hubei Normal University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this paper,we focus on the tracking performance(TP) limitations of networked control systems(NCS s).The control system considered is a multi-input and multi-output(MIMO) discrete-time network control system with time delay,channel noise,packet dropouts,and logarithmic quantization communication *** modeling the channel,we assumed the channel noise to be additive white Gaussian noise(AWGN) and used a binary random process to model packet dropouts and a logarithmic quantizer to model the quantization *** explicit expression of the TP limitations of NCSs utilizing the two-degree-of-freedom(TDOF) controller is then derived using the frequency-domain approach,coprime decomposition,and Youla parameterization *** results show that the essential characteristics of the plant(locations and directions of nonminimum phase zeros and unstable poles) and network constraints(time delay,packet dropouts,quantization,and noise power spectral density) are closely related to the TP limitations.

关键词： Networked control systems tracking performance limitations packet dropouts logarithmic quantization

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Study on Landslide Susceptibility Evaluation Based on Improved Multivariable Weighted Model: A Case Study of Lushan County, China 3

Study on Landslide Susceptibility Evaluation Based on Improv...

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3rd IEEE International Conference on Software Engineering and Artificial Intelligence, SEAI 2023

作者： Zhao, Li Duan, Gonghao Wuhan Institute of Technology School of Computer Science and Engineering Wuhan China Automation for Complex Systems Hubei Key Laboratory of Advanced Control and Intelligent Wuhan China

ISBN: (纸本)9798350337952

The evaluation of regional geological hazard susceptibility is of great significance to the prevention and control of geological hazard. In this paper, the "4-20"Lushan earthquake disaster area as the research area, combined with GIS and characteristics of the research area, through correlation analysis, selected 9 influencing factors as the evaluation factors. The study was conducted using a weighted information volume-logistic regression model (WI-LR). The results were classified into five sensitivity levels: very low, low, medium, high and very high. The results show that the landslide prone areas in Lushan County are concentrated in the area below the middle of the county seat, mostly near rivers, faults and areas with peak acceleration greater than 0.4. Among them, WI-LR model (AUC=0.918) > deterministic factor model (CF =0.908) > weighted information quantity model (WI) (AUC=0.896) > information quantity model I (AUC=0.853) > information quantity logistic regression model I-LR (AUC=0.799). It indicates that WI-LR model has high evaluation accuracy. © 2023 IEEE.

关键词： Hazards

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Optimal Tracking Performance of Networked control systems Under DoS and Its Applications

Optimal Tracking Performance of Networked Control Systems Un...

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2024 China automation Congress, CAC 2024

作者： Ren, Xinyu Li, Bo Zhao, Feng Chen, Xiangyong Zhang, Xianhe Jiang, Xiaowei School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Finance Anhui University of Finance and Economics Bengbu China Key Laboratory of Complex systems and Intelligent Computing in Universities of Shandong Linyi University Linyi China School of Automation and Electrical Engineering Key Laboratory of Complex systems and Intelligent Computing in Universities of Shandong Linyi University Linyi China School of Electrical Engineering and Automation Hubei Normal University Huangshi China

ISBN: (纸本)9798350368604

This paper investigates the optimal tracking performance (OTP) of multiple-input multiple-output (MIMO) discrete networked control systems (NCSs) under the influence of Denial of Service (DoS) attack, channel noise, and bandwidth constraints. The channel noise is assumed to be additive Gaussian white noise (AWGN). Due to the randomness of DoS attack, a Bernoulli distribution is used for DoS modeling. Utilizing frequency domain analysis methods, coprime factorization, all-pass decomposition, and Youla parameterization, an explicit expression for the optimal tracking performance (OTP) of the system under the two-degree-of-freedom (TDOF) controller is derived. The analysis indicates that non-minimum phase (NMP) zeros, unstable poles (UPs), DoS attack, and network constraints affect the OTP of NCSs. Finally, the theoretical results are validated through simulations of MIMO NCS and vehicle control system. © 2024 IEEE.

关键词： White noise

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Research and application of composite stochastic resonance in enhancement detection

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Chinese Physics B 2024年第1期33卷 264-273页

作者：高蕊焦尚彬薛琼婕 School of Automation and Information Engineering Xi'an University of TechnologyXi'an 710048China School of Electronic and Electrical Engineering Baoji University of Arts and SciencesBaoji 721016China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of TechnologyXi'an 710048China

Aiming at the problem that the intermediate potential part of the traditional bistable stochastic resonance model cannot be adjusted independently, a new composite stochastic resonance(NCSR) model is proposed by combining the Woods–Saxon(WS) model and the improved piecewise bistable model. The model retains the characteristics of the independent parameters of WS model and the improved piecewise model has no output saturation, all the parameters in the new model have no coupling characteristics. Under α stable noise environment, the new model is used to detect periodic signal and aperiodic signal, the detection results indicate that the new model has higher noise utilization and better detection ***, the new model is applied to image denoising, the results showed that under the same conditions, the output peak signal-to-noise ratio(PSNR) and the correlation number of NCSR method is higher than that of other commonly used linear denoising methods and improved piecewise SR methods, the effectiveness of the new model is verified.

关键词： Woods–Saxon improved piecewise model composite stochastic resonance(SR) image denoising

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