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第35届中国控制与决策会议

作者： Ben Wu Wei Liu Weijie Sun Key Laboratory of Autonomous Systems and Networked Control Ministry of EducationSchool of Automation Science and EngineeringSouth China University of Technology

In this paper,by using the output regulation theory,we research the output voltage practical tracking problem for uninterruptible power supply systems under event-triggered control *** configuring the desired poles for a controllable pair and solving the regulator equations,a state feedback gain and a feedforward gain can be obtained for designing the control ***,under event-triggered mechanism,a control law that can be applied to a digital platform directly is designed to solve our ***,it can be verified that the voltage tracking error can converge to a pre-specified neighborhood of the origin.

关键词： Uninterruptible power supply output regulation event-triggered control

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A Ducted Fan UAV for Safe Aerial Grabbing and Transfer of Multiple Loads Using Electromagnets

A Ducted Fan UAV for Safe Aerial Grabbing and Transfer of Mu...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Zhong Yin Hailong Pei Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Guangdong Engineering Technology Research Center of Unmanned Aerial Vehicle Systems South China University of Technology Guangzhou China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In recent years, research on aerial grasping, manipulation, and transportation of objects has garnered significant attention. These tasks often require UAVs to operate safely close to environments or objects and to efficiently grasp payloads. However, current widely adopted flying platforms pose safety hazards: unprotected high-speed rotating propellers can cause harm to the surroundings. Additionally, the space for carrying payloads on the fuselage is limited, and the restricted position of the payload also hinders efficient grasping. To address these issues, this paper presents a coaxial ducted fan UAV which is equipped with electromagnets mounted externally on the fuselage, enabling safe grasping and transfer of multiple loads in midair without complex additional actuators. It also has the capability to achieve direct human-UAV cargo transfer in the air. The forces acting on the loads during magnetic attachment and their influencing factors were analyzed. An ADRC controller is utilized to counteract disturbances during grasping and achieve attitude control. Finally, flight tests are conducted to verify the UAV’s ability to directly grasp multiple loads from human hands in flight while maintaining attitude tracking.

关键词： Fans Propellers Transportation Grasping Electromagnets autonomous aerial vehicles Safety Magnetic analysis Intelligent robots Payloads

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Cascading Time-Frequency Transformer and Spatio-Temporal Graph Attention Network for Rotating Machinery Fault Diagnosis

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷 1页

作者： Liu, Yiqi Yu, Zhewen Xie, Min South China Univ Technol Sch Automat Sci & Engn Key Lab Autonomous Syst & Networked Control Minist Educ Guangzhou 510640 Peoples R China South China Univ Technol Unmanned Aerial Vehicle Syst Engn Technol Res Ctr Sch Automat Sci & Engn Guangzhou 510640 Peoples R China City Univ Hong Kong Sch Syst Engn Kowloon Tong Hong Kong Peoples R China

Rotating machinery fault diagnosis is of great importance to guarantee safe and optimal operations of industrial processes. Heavy noise and dynamic behaviors usually make accurate mechanical fault diagnosis impossible while using the standard methodologies, particularly when they disregard specific domain information related to time, frequency, or space. To address these challenges, we propose a novel model, called spatio-temporal-frequency graph attention network (STFGAT), which can integrate time domain, frequency domain, and spatial information. The model leverages the Transformer to encode time and frequency information, then refined complex patterns in the time and frequency domain through self-attention mechanism and frequency domain attention, and finally captures the hidden patterns behind the data through the collaboration of time and frequency information. The encoded information is subsequently fed into the spatio-temporal graph attention network (STGAT) to allow the model to take full use of the spatial relationships between different components of the mechanical system and the temporal relationships across various time lags. This process improvement can learn complex patterns and relationships within the data, thereby facilitating predictions regarding the system's state. The experimental results show that STFGAT outperforms other standard diagnostic models in the case studies and can achieve better diagnostic accuracy.

关键词： graph neural network (GNN) rotating machinery transformer graph neural network (GNN) rotating machinery Fault diagnosis Fault diagnosis

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A Semisupervised Soft-Sensor of Just-in-Time Learning With Structure Entropy Clustering and Applications for Industrial Processes Monitoring

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第4期4卷 722-733页

作者： Li, Dong Liu, Yiqi Huang, Daoping Xu, Chong South China University of Technology Key Laboratory of Autonomous Systems and Networked Control Ministry of Education School of Automation Science and Engineering Guangzhou510640 China Technical University of Denmark Process and Systems Engineering Center Department of Chemical and Biochemical Engineering Lyngby2800 Denmark South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Gannan Normal University School of Physics and Electronic Information Ganzhou341000 China

To monitor industrial processes properly, soft-sensors are widely used to predict significant but difficult-to-measure quality variables. However, the prediction performances of traditional data-driven soft-sensors are usually unacceptable once suffering from high-nonlinear, high-dimension, and imblance data issues. Therefore, a semisupervised soft-sensor, which is learned by a just-in-time method with structure entropy clustering (SS-JITL-SEC), is proposed aiming to improve prediction performance with a simpler way. Inspired by a divide and conquer strategy, a novel SEC method is proposed to achieve several clusters and then to translate the highly complex and nonlinear modeling problems into simple and linear ones. Moreover, the training dataset is extended through a mixed SS labeling approach. Finally, dissimilarity-based JITL works together with the resulting clustering subdatasets to formulate a local adaptive prediction model. Two datasets from different types of wastewater treatment plants are used to verify the effectiveness of the proposed soft-sensor. The results show that the SS-JITL-SEC soft-sensor can achieve better prediction performance than other standard counterparts, and even for effective process monitoring with the resulted residuals. © 2020 IEEE.

关键词： Entropy

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Towards sludge bulking diagnosis via dynamic attention graph neural network

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JOURNAL OF WATER PROCESS ENGINEERING 2025年 69卷

作者： Chen, Yan Huang, Daoping Wu, Jing Fang, Gang Li, Yan Aman, Abid Liu, Yiqi South China Univ Technol Sch Automat Sci & Engn Key Lab Autonomous Syst & Networked Control Minist Educ Guangzhou 510640 Peoples R China South China Univ Technol Unmanned Aerial Vehicle Syst Engn Technol Res Ctr Sch Automat Sci & Engn Guangzhou 510640 Peoples R China Guizhou Minzu Univ Sch Data Sci & Informat Engn Guiyang 550025 Peoples R China

Sludge bulking is a critical issue in activated sludge-based wastewater treatment processes, leading to severe problems with solids settling that pose threats to environmental sustainability, public health, and economic efficiency. Existing traditional statistical models often struggle to identify early-stage sludge bulking due to their inability to dynamically account for variations in performance. To address these shortcomings, we propose a novel Dynamic Graph Convolutional Network (DA-GCN) enhanced with an attention mechanism that effectively detects incipient faults related to sludge bulking. This model utilizes graph structures to represent the complex interrelationships and temporal dynamics of time-series data, leveraging lagged observations to create adjacency matrices that reflect the system's behavior over time. By reconstructing node features through two-hop neighbor connections, the DA-GCN learns to identify key dependencies, and the application of attention mechanisms helps prioritize significant relationships, which improves the detection of subtle faults often masked by noise. We validate the effectiveness of this approach using the Benchmark Simulation Model No. 2 (BSM2) to simulate various sludge bulking scenarios, as well as data from a real-world wastewater treatment plant experiencing these challenges. Experimental findings demonstrate that the DA-GCN outperforms traditional models in managing dynamic data, presenting a reliable and innovative tool for the early diagnosis and classification of sludge bulking issues.

关键词： Graph convolutional network (GCN) Sludge bulking Fault diagnosis Attention mechanism Incipient faults

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Adaptive Image-Based Moving-Target Tracking control of Wheeled Mobile Robots With Visibility Maintenance and Obstacle Avoidance

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IEEE TRANSACTIONS ON control systems TECHNOLOGY 2024年第2期32卷 488-501页

作者： Dai, Shi-Lu Liang, Jianjun Lu, Ke Jin, Xu South China Univ Technol Sch Automat Sci & Engn Key Lab Autonomous Syst & Networked Control Minist Educ Guangzhou 510641 Peoples R China Southern Marine Sci & Engn Guangdong Lab Zhuhai Zhuhai 519000 Peoples R China South China Univ Technol Sch Automat Sci & Engn Guangzhou 510641 Peoples R China Shenzhen Dajiang Innovat Technol Co Ltd Shenzhen 518057 Peoples R China Univ Kentucky Dept Mech & Aerosp Engn Lexington KY 40506 USA

This article presents an adaptive image-based visual servoing (IBVS) for nonholonomic wheeled mobile robots to solve the moving-target tracking problem in obstacle environments. A pinhole camera that is equipped with the following robot to monitor the target' motion is limited field of view (FOV), which relates to the issue of visibility maintenance. Safety navigation is another concern, which requires that the following robot can avoid collisions with the target and static obstacles. Under the IBVS framework, we design novel constrained boundary functions based on pixel coordinate, which can be deviated away from zero to ensure that the following robot drives away from obstacles because obstacle avoidance is a higher priority rather than the tracking task. When there is no obstacle detected, the constrained boundary functions are taken as exponentially decaying functions of time. Using fixed-time stability and control Lyapunov synthesis, the tracking errors are shown to converge in fixed time to a small neighborhood of the desired obstacle-avoidance trajectory generated from the centerline of constrained boundaries while guaranteeing visibility maintenance and obstacle/collision avoidance. The proposed fixed-time IBVS controller (FTIBVSC) only depends on locally relative information acquired by onboard sensors without the need of knowing the feature height and target's velocity. Simulation and experiment studies are carried out to show the efficacy of the proposed FTIBVSC.

关键词： Collision avoidance Robot kinematics Target tracking Mobile robots Maintenance engineering Cameras control design Fixed-time control image-based visual servoing (IBVS) moving-target tracking obstacle avoidance visibility maintenance

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A Ducted Fan UAV for Safe Aerial Grabbing and Transfer of Multiple Loads Using Electromagnets

arXiv

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arXiv 2024年

作者： Yin, Zhong Pei, Hailong Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Guangdong Engineering Technology Research Center of Unmanned Aerial Vehicle Systems South China University of Technology Guangzhou510640 China

关键词： Unmanned aerial vehicles (UAV)

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Sliding mode observer-based model predictive tracking control for Mecanum-wheeled mobile robot

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ISA TRANSACTIONS 2024年 151卷 51-61页

作者： Wang, Dongliang Gao, Yong Wei, Wu Yu, Qiuda Wei, Yuhai Li, Wenji Fan, Zhun Shantou Univ Sch Dept Elect & Informat Engn Shantou 515063 Guangdong Peoples R China Shantou Univ Key Lab Digital Signal & Image Proc Guangdong Prov Shantou 515063 Guangdong Peoples R China South China Univ Technol Sch Automat Sci & Engn Guangzhou 510641 Guangdong Peoples R China Minist Educ Key Lab Autonomous Syst & Networked Control Guangzhou 510640 Guangdong Peoples R China Univ Elect Sci & Technol China Shenzhen Inst Adv Study Shenzhen 518038 Guangdong Peoples R China

This paper proposes a novel adaptive variable power sliding mode observer-based model predictive control (AVPSMO-MPC) method for the trajectory tracking of a Mecanum-wheeled mobile robot (MWMR) with external disturbances and model uncertainties. First, in the absence of disturbances and uncertainties, a model predictive controller that considers various physical constraints is designed based on the nominal dynamics model of the MWMR, which can transform the tracking problem into a constrained quadratic programming (QP) problem to solve the optimal control inputs online. Subsequently, to improve the anti-jamming ability of the MWMR, an AVPSMO is designed as a feedforward compensation controller to suppress the effects of external disturbances and model uncertainties during the actual motion of the MWMR, and the stability of the AVPSMO is proved via Lyapunov theory. The proposed AVPSMO-MPC method can achieve precise tracking control while ensuring that the constraints of MWMR are not violated in the presence of disturbances and uncertainties. Finally, comparative simulation cases are presented to demonstrate the effectiveness and robustness of the proposed method.

关键词： Mecanum-wheeled mobile robot Model predictive control Sliding mode observer Trajectory tracking

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Predefined Time Attitude control for Quadrotor UAV with Output Constraints and Input Saturation

Predefined Time Attitude Control for Quadrotor UAV with Outp...

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Chinese Automation Congress (CAC)

作者： Shuo Li Hailong Pei Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Guangdong Engineering Technology Research Center of Unmanned Aerial Vehicle Systems College of Automation Science and Enginneering South China University of Technology Guangzhou China Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Guangdong Engineering Technology Research Center of Unmanned Aerial Vehicle Systems School of Automation Science and Enginneering South China University of Technology Guangzhou China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

In this paper, we examine the predefined time flight control of a quadrotor UAV with output constraints and input saturation. The attitude tracking error of UAV is constrained by the tan-type barrier Lyapunov function. The undesirable effects of the saturated input limitations are addressed by introducing an auxiliary system. Then a predefined time tracking control strategy is designed and the stability of the scheme is analyzed. Eventually, the simulation results show the method allows the angular tracking error to converge to the specified range in a predefined time.

关键词： Backstepping Automation Attitude control Simulation autonomous aerial vehicles Stability analysis Quadrotors Lyapunov methods

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Design and Implementation of a Group control Communication System for Intelligent Rubber Tapping Robots 42

Design and Implementation of a Group Control Communication S...

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42nd Chinese control Conference, CCC 2023

作者： Liu, Fuchun Jiang, Dong Chen, Xiaoyang Chen, Xujian Huang, Shengfeng School of Automation Science and Engineering South China University of Technology Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong Guangzhou510640 China Beijing Normal University Hong Kong Baptist United International College ZhuHai519087 China

ISBN: (纸本)9789887581543

With the development and promotion of electric rubber tapping equipment, China has gradually mechanized rubber tapping, but the degree of automation could be higher. This paper designs an intelligent rubber tapping remote monitoring system based on Internet of Things(IoT) technology that is applied to single-motor driven rubber tapping robots and uploads the status data of robots in the rubber plantation to the cloud database for storage, data analysis, and display in the backend management system in order to realize the intelligent and digital upgrade of rubber tapping robots. It can achieve remote group control of the robots in the rubber plantation at the same time using a cloud-based human-machine interface. The system consists of a terminal electronic control subsystem, a communication subsystem, and a cloud-based backend management system. This system uses LoRa RF technology and Narrowband IoT(NBIoT) technology to design a star-shaped network to connect the robots to the cloud for unified management and provide data support for later tapping decisions. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Rubber

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