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Robust control for Discrete-Time T-S Fuzzy Singular Systems

Journal of Systems Science & Complexity 2021年第4期34卷 1345-1363页

作者： CHEN Jian YU Jinpeng School of Information and Control Engineering Qingdao University of TechnologyQingdao 266520China School of Automation Qingdao UniversityQingdao 266071China

This paper deals with the robust admissibility and state feedback stabilization problems for discrete-time T-S fuzzy singular systems with norm-bounded *** introducing a new approximation technique,the initial membership functions are conveniently expressed in piecewiselinear functions with the consideration of the approximation *** utilizing the piecewise-linear membership functions,the fuzzy weighting-based Lyapunov function and the use of auxiliary matrices,the admissibility of the systems is determined by examining the conditions at some sample *** conditions can be reduced into the normal parallel distributed compensation ones by choosing special values of some slack ***,the authors design the robust state feedback controller to guarantee the closed-loop system to be *** examples are provided to illustrate the advantage and effectiveness of the proposed method.

关键词： Admissible discrete-time singular systems piecewise-linear membership functions state feedback T-S fuzzy models

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SDSFusion: A Semantic-Aware Infrared and Visible Image Fusion Network for Degraded Scenes

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IEEE Transactions on Image Processing 2025年 34卷 3139-3153页

作者： Chen, Jun Yang, Liling Yu, Wei Gong, Wenping Cai, Zhanchuan Ma, Jiayi 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 Ministry of Education Wuhan430074 China China University of Geosciences Faculty of Engineering Wuhan430074 China Macau University of Science and Technology School of Computer Science and Engineering Taipa China Wuhan University Electronic Information School Wuhan430072 China

A single-modal infrared or visible image offers limited representation in scenes with lighting degradation or extreme weather. We propose a multi-modal fusion framework, named SDSFusion, for all-day and all-weather infrared and visible image fusion. SDSFusion exploits the commonality in image processing to achieve enhancement, fusion, and semantic task interaction in a unified framework guided by semantic awareness and multi-scale features and losses. To address the disparity between infrared and visible images in degraded scenes, we differentiate modal features in a unified fusion model. Unlike existing joint fusion methods, we propose an adversarial generative network that refines the reconstruction of low-light images by embedding fused features. It provides feature-level brightness supplementation and image reconstruction to refine brightness and contrast. Extensive experiments in degraded scenes confirm that our approach is superior to state-of-the-art approaches in visual quality and performance, demonstrating the effectiveness of interaction improvement. © 1992-2012 IEEE.

关键词： Image coding

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Research on the identification method of heavy haul railway rail surface injury study based on the fusion of vibration and profile data 6

Research on the identification method of heavy haul railway ...

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6th International Conference on Information Science, Electrical, and automation engineering, ISEAE 2024

作者： Meng, Xiangrui Sun, Yongkui Luo, Chenshuo Yang, Fengbin An, Yuntong School of Automation and Intelligence Beijing Jiaotong University Beijing100044 China National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9781510682924

Due to the heavy haul rail's large single train capacity and strong impact on the track, the rails of heavy haul rail are prone to injuries, and the accurate detection and identification of injuries is the basis of intelligent operation and maintenance of the rails of heavy haul rail. However, the existing rail surface injury recognition methods mainly based on machine vision have the disadvantages of strong noise and limited field of view. Therefore, this paper proposes a detection method based on the fusion of profile data and vibration signals. The profile data has high accuracy, but it has the disadvantages of low detection rate and low accuracy of a single profile. The vibration signal is easy to measure, but it is easy to be interfered, and the vibration signals of some injuries will reflect the same characteristics. The respective defects are overcome by fusion of the two. The experimental results show that the detection accuracy is effectively improved after the fusion of the two. The vibration signals are extracted and dimensionality reduced using stacked self-coding network, and then input into the classifier to obtain the classification results based on the vibration data. At the same time, the profile data and standard rails are matched, and then the wear sequence is generated by the DTW algorithm, and then the features are extracted by the PCA algorithm and classified to obtain the results based on the profile data. Finally, the two methods are fused at the decision level to obtain a new rail surface injury recognition and detection method based on the fusion of the two. © 2024 SPIE.

关键词： Data fusion

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Multi-Agent Cooperative Localization via Extraction and Matching of Image Feature 41

Multi-Agent Cooperative Localization via Extraction and Matc...

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第41届中国控制会议

作者： Xiong Lei Yulin Xu 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

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In multi-agent system(MAS),it is of great significance to study the problem of cooperative *** this work,we propose a vision-based cooperative localization scheme for multi-agent systems equipped with RGB-D ***,we formulate the cooperative localization problem of ***,the relative pose between agents with overlapping field of view in MAS can be solved by using an improved feature extraction and matching algorithm,which requires the index function to screen the unimportant feature points in the image to reduce the computation *** system does not require all agents to have a common area of view,and we can calculate the relative pose between agents whose field of view does not overlap at all by accumulating the ***,experiments are carried out to validate the effectiveness and feasibility of this method.

关键词： Multi-agent system Cooperative localization Feature extraction and matching Index function

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Distributed control Algorithm for Workload Partition of Multi-agent Systems with Time Delay 41

Distributed Control Algorithm for Workload Partition of Mult...

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第41届中国控制会议

作者： Pengyang Fan 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

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In the problem of multi-agent coverage,a group of agents are required to cover a given region or clean the workload as soon as *** workload partition can improve the work efficiency of multi-agent *** addition,the communication delays play a key role in the stability of multi-agent ***,a distributed control algorithm of workload partition with time delay is proposed in this ***,an approach based on nonlinear matrix inequality is presented to find the maximum delay of multi-agent ***,experiments are carried out to investigate the effect of delay on system stability on the ZigBee platform.

关键词： Multi-agents systems Time delay Nonlinear matrix inequality Distributed algorithm Workload partition

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An Inverse Kinematic Optimization Method for the S-R-S Redundant Manipulator Considering Joint Limit and Singular Position Avoidance 41

An Inverse Kinematic Optimization Method for the S-R-S Redun...

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第41届中国控制会议

作者： Yisheng Mei Xin Chen 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

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Aiming at the problem of how to avoid the joint limit and singular position in continuous trajectory planning of the S-R-S redundant manipulator,a new inverse kinematics optimization algorithm is proposed in this ***,according to the special geometry of S-R-S manipulator,we take the arm angle as the redundant parameter to obtain the joint angle expressions under the given tip pose and arm *** we discuss the feasible arm angles under joint limit in nonsingular and singular cases,and give a complete method to obtain the effective arm ***,we introduce the concept of virtual force to represent the degree that the joint is far from the limit,and define the optimization objective function with the virtual force as the weight to avoid joint limit,the complex relationship between the arm angle and joint angles is linearized by the difference method to simplify the objective function to quadratic function,combine with the feasible arm angle range,we realize singularity avoidance and joint limit avoidance at the same *** simulation result shows that compared with the traditional algorithm,the algorithm proposed in this paper has better joint limit avoidance ability on the premise of avoiding singularity which reflects the superiority of the algorithm and can be used for the motion planning of the manipulator in practical engineering.

关键词： Arm angle Singular position avoidance Joint limit avoidance Inverse kinematic optimization

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Detection of Perfect Stealthy Attacks on Cyber-Physical Systems Subject to Measurement Quantizations: A Watermark-Based Strategy

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 114-125页

作者： Yu-Ang Wang Zidong Wang Lei Zou Bo Shen Hongli Dong the College of Information Science and Technology Donghua University the Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of Education IEEE the College of Electrical Engineering and Automation Shandong University of Science and Technology the Department of Computer Science Brunel University London Uxbridge the Sanya Offshore Oil and Gas Research Institute Northeast Petroleum University the Artificial Intelligence Energy Research Institute Northeast Petroleum University Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum University

In this paper, the attack detection problem is investigated for a class of closed-loop systems subjected to unknownbutbounded noises in the presence of stealthy attacks. The measurement outputs from the sensors are quantized before transmission.A specific type of perfect stealthy attack, which meets certain rather stringent conditions, is taken into account. Such attacks could be injected by adversaries into both the sensor-toestimator and controller-to-actuator channels, with the aim of disrupting the normal data flow. For the purpose of defending against these perfect stealthy attacks, a novel scheme based on watermarks is developed. This scheme includes the injection of watermarks(applied to data prior to quantization) and the recovery of data(implemented before the data reaches the estimator).The watermark-based scheme is designed to be both timevarying and hidden from adversaries through incorporating a time-varying and bounded watermark signal. Subsequently, a watermark-based attack detection strategy is proposed which thoroughly considers the characteristics of perfect stealthy attacks,thereby ensuring that an alarm is activated upon the occurrence of such attacks. An example is provided to demonstrate the efficacy of the proposed mechanism for detecting attacks.

关键词： Attack detection cyber-physical systems (CPSs) perfect stealthy attacks watermark-based strategy

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A Deep Learning Approach for Faults Recognition of Dissolved Oxygen Sensor in Wastewater Treatment Plants 28

A Deep Learning Approach for Faults Recognition of Dissolved...

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28th IEEE International Conference on Emerging Technologies and Factory automation, ETFA 2023

作者： Ghinea, Liliana Maria Miron, Mihaela Ratnaweera, Harsha Electrical Engineering and Electronics Faculty of Automation Computers Department of Automatic Control and Electrical Engineering Department Galaţi Romania Computers Electrical Engineering and Electronics Faculty of Automation Department of Computer Science and Information Technology Galaţi Romania Norwegian University of Life Sciences Faculty of Science and Technology Oslo Norway

ISBN: (纸本)9798350339918

Fault diagnosis in wastewater treatment plants (WWTPs) is important to protect communities and ecosystems from toxic elements discharged into water. In this sense, fault identification of sensors plays an important role as they are the key components of the water plants control, especially because environmental legislation is very strict when referring to failures or anomalies in WWTPs. This paper analyzes the performances of two Deep Learning models, a Feedforward Neural Network (FFNN) and a 1D Convolution Neural Network (1DCNN) for identifying five operating states of the dissolved oxygen (DO) sensor: normal and faulty (bias, stuck, spike and precision degradation faults). The experiments were conducted on the Benchmark Simulator Model No 2 (BSM2) developed by the IWA Task Group. The performance of the Deep Learning (DL) classifiers was evaluated via accuracy, precision, recall, and F1-score metrics. The best overall classification accuracy was obtained by FFNN, 98.32% for training and 98.30% for testing. © 2023 IEEE.

关键词： Wastewater treatment

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Artificial Intelligence-based Fault Detection and Diagnosis: Towards Application in a Chemical Process

Artificial Intelligence-based Fault Detection and Diagnosis:...

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2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2023

作者： Liu, Yiqi Li, Zhi Chen, Hongtian The School of Automation Science & Engineering Key Laboratory of Autonomous Systems and Networked Control Ministry of Education South China University of Technology Guangzhou510640 China Shanghai Jiao Tong University Department of Automation Shanghai200240 China

ISBN: (纸本)9798350337754

As the chemical processes become more and more complicated, it is crucial but also difficult to detect and diagnose faults in the system than ever. With the development of machine learning algorithms and cloud computing, to monitor chemical processes completely with the help of big data become true. However, most machine learning methods suffer from unexplainable problem. By employing Explainable Artificial Intelligence(XAI) techniques, this paper applies two kinds of unsupervised machine learning methods, One-Class Support Vector Machine and Depth-based Isolation Forest Feature Importance, to detect and diagnosis faults in a chemical process. By combining two different fault explanation methodologies, accuracy and reliability of the fault diagnosis results can be achieved by the validation in a simulated continuous stirred tank reactor. © 2023 IEEE.

关键词： Support vector machines

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Autoregressive Minimum Entropy Deconvolution Enhanced Resonance Features for Fault Diagnosis of Wind Turbine Gearbox

Autoregressive Minimum Entropy Deconvolution Enhanced Resona...

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2023 International Conference on automation, control and Electronics engineering, CACEE 2023

作者： Shi, Xiaoyu Sun, Kang Henan Polytechnic University School of Electrical Engineering and Automation Jiaozuo China Henan Polytechnic University Dianrong Intelligent Technology Co. Ltd. School of Electrical Engineering and Automation Henan Key Laboratory of Intelligent Detection and Control of Coal Mine Equipment Jiaozuo China

ISBN: (纸本)9798350309201

Addressing the problem that the pulse component often submerges in other frequency components during the failure of the wind turbine gearbox, it is difficult to extract effectively the early fault characteristics. This paper proposed autoregressive minimum entropy deconvolution enhanced resonance features for wind turbine gearbox fault diagnosis. The method is combined with 1DCNN to achieve high-accuracy fault diagnosis. Firstly, the resonance-based sparse signal decomposition was utilized to decompose the vibration signals into a high resonance component containing noise and harmonic components and a low resonance component containing fault impulse components. Secondly, the AR-MED was employed to enhance the weak impulse features in the low resonance component, thus further enhancing the periodic impulse components of the gearbox. Finally, a feature enhanced 1DCNN was constructed to fuse the decomposed harmonic and periodic impulse components for feature integration, targeted training, and classification. The outcomes of the experiment indicated the efficacy and supremacy of the suggested approach in extracting information about fault characteristics and enhancing the accuracy of fault diagnosis in wind turbine gearbox systems in contrast to current fault diagnosis models. © 2023 IEEE.

关键词： Failure analysis

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