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IEEE International Conference on Unmanned Systems (ICUS)

作者： Yueqi Yang Tao Yan Jiajie Shen Guoqiang Sun Zhenpo Tian Wen Ju School of Automation Science and Engineering Xi'an Jiaotong University Xi'an China Beijing Aerospace Automatic Control Institute Beijing China

ISBN: (数字)9798350384185

ISBN: (纸本)9798350384192

This paper addresses the challenge of group targets tracking in complex environments, aiming to enhance the real-time performance and accuracy of tracking systems by optimizing multi-dimensional assignment strategy for selecting the globally optimal hypothesis. A new tracking algorithm named Ballistic Track-Oriented MHT (BTOMHT) is proposed. This algorithm introduces Lagrangian multipliers to achieve effective dimensionality reduction, thereby significantly decreases the computation complexity of data association in multi-dimensional spaces for group targets. Simulations demonstrate that compared with the Global Nearest Neighbor algorithm, BTOMHT not only sustains high precision but also has a great performance in complex dynamic tracking scenarios. It can enhance the efficiency of both target detection and tracking and concurrently bolster overall system stability in tracking tasks. The efficiency of the proposed algorithm is validated across two assessment indices. The algorithm contributes to the theoretical framework of multi-target tracking and furnishes technical support for applications in military defense.

关键词： Missiles Target tracking Accuracy Heuristic algorithms Stability criteria Estimation Object detection Robustness Real-time systems Maintenance

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Fixed-Time Inter-Axis Synchronization control of Manipulators based on Active Disturbance Rejection Method

Fixed-Time Inter-Axis Synchronization Control of Manipulator...

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Data Driven control and Learning Systems (DDCLS)

作者： Hanpu Zhao Yang Liu Seventh Research Division Center for Information and Control School of Automation Science and Electrical Engineering Beihang University (BUAA) Beijing China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

This paper investigates the fixed-time inter-axis synchronization tracking control for n-degree of freedom (DOF) uncertain manipulators. First, a dynamic model for the manipulator is presented, and a fixed-time disturbance observer is designed to compensate for system uncertainties and external disturbances. Then, a non-singular fixed-time active disturbance rejection inter-axis synchronization controller is designed, which not only avoids the singularity problem in traditional terminal sliding mode control, but also realizes the active suppression of disturbances. It is proved that the tracking error of each joint of the uncertain manipulator converges synchronously to zero within a fixed time. Finally, simulations are conducted with a 2-DOF manipulator, and results demonstrate that the method can achieve fixed-time inter-axis synchronization control and possesses robustness against the lumped disturbances.

关键词： Learning systems Uncertainty Trajectory tracking 2-DOF Disturbance observers Stability analysis Robustness

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A Smart Grid Traffic Anomaly Detector Based on Deep Learning

A Smart Grid Traffic Anomaly Detector Based on Deep Learning

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2022 International Conference on Frontiers of Communications, Information System and Data science, CISDS 2022

作者： Xiong, Wei Lei, Yu Zhou, Yuqing Zhang, Yachao Wei, Su Liu, Zhe State Grid Chongqing Electric Power Company Automation Department of Control Center Chongqing China Wuhan University School of Cyber Science and Engineering Wuhan China

ISBN: (纸本)9781665464468

The information network of the power system is an important part of the long- term continuous and effective operation in power industry. The complex network structure between the power grid and the information network in the smart grid brings a great challenge to the abnormal detection of network flow in the security of information communication network. However, traditional machine learning algorithms often have shortcomings such as low detection accuracy and poor real-time performance in solving the problem of network flow anomaly detection, and they are not possible to provide a high-precision early warning function for network security events in advance. In this paper, we introduce the principle of traffic anomaly detection technology based on intrusion detection, and propose a deep learning predictor for abnormal traffic in grid system. By learning the temporal and spatial features of traffic data, deep learning predictor can predict traffic features in the next period and classify traffic security events for the predicted traffic. To a certain extent, it can meet the demand of abnormal traffic detection of smart grid servers, so as to achieve the purpose of improving power grid information security. © 2022 IEEE.

关键词： Smart power grids

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Barrier Offset Varying-Parameter Dynamic Learning Network for Solving Dual-Arms Human-Like Behavior Generation

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IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Zhang, Zhijun Zhang, Mingyang Guo, Jinjia He, Haotian School of Automation Science and Engineering South China University of Technology Guangzhou510640 China Key Library of Autonomous Systems and Network Control Ministry of Education China Jiangxi Thousand Talents Plan Nanchang University Nanchang330031 China Guangzhou510335 China Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Shanxi University of Technology Hanzhong723001 China School of Information Science and Engineering Changsha Normal University Changsha410100 China School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Guangdong University of Petrochemical Technology Maoming525000 China School of Computer Science and Engineering Jishou University Jishou416000 China

Enabling robots to imitate human actions and perform tasks with high precision while avoiding potential obstacles in the environment can effectively enhance the interaction between social robots and humans. In this paper, to achieve higher precision trajectory tracking and obstacle avoidance for dual-arm humanoid robots, the barrier offset varying-parameter dynamic learning neural (BOVDL) network method is proposed and applied to dual-arm humanoid behavior generation scheme. To do so, a dual-arms humanoid robot model is set up, and transformed into a constrained time-varying quadratic programming (TVQP) problem. Secondly, by using lagrangian multiplier method and Karush-Kuhn-Tuchker condition, the inequality constrained TVQP is converted as a time-varying equation with a barrier parameter. Thirdly, a varying-parameter dynamic learning network is presented to solve the time-varying equation with a barrier parameter. Computer simulation experiments are conducted to verify the feasibility, accuracy, and safety of the proposed BOVDL network method. Experimental results show that all 14 joints of the humanoid robot's arms are within the motion range of each real human arm's physical constraints. The maximum position error and velocity error between the desired trajectory and the actual trajectory of the end effector are less than 10-6m magnitude and 10-7m magnitude, respectively, representing a reduction of 5 orders of magnitude compared to the traditional varying-parameter convergent-differential neural network. Furthermore, the proposed method also enables the dual-arm humanoid robot to avoid collisions with obstacles while performing tasks, demonstrating the superiority of the proposed BOVDL network scheme. © 2016 IEEE.

关键词： Anthropomorphic robots

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Dynamic Quantization based control Synthesis for Signal Temporal Logics Specifications

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IFAC-PapersOnLine 2023年第2期56卷 10414-10420页

作者： Li Tan Wei Ren Xi-Ming Sun Junlin Xiong Department of Automation University of Science and Technology of China Hefei 230026 China School of Control Science and Engineering Dalian University of Technology Dalian 116033 China.

For dynamical systems under temporal logic tasks, the control objective is mainly to propose control methods for temporal logic tasks. Existing methods are based on the assumption where temporal logic tasks are realizable, which is not always valid and results in the separation between the realizability verification and the controller design. In this paper, we apply dynamic quantization techniques to propose a framework to combine the verification of signal temporal logic (STL) specifications and the controller design. In particular, a sequence of quantization regions is generated to show the realizability of the STL formula, and results in the decomposition of the global STL formula into finite local ones. All local STL formulas are formulated into optimization problems, which are solved iteratively and result in local controllers, whose combination gives the global controller for the satisfaction of the STL formula. Finally, a numerical example is presented to illustrate the proposed approach.

关键词： controller design nonlinear systems dynamic quantization signal temporal logic

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Object Recognition and Grasp Detection in Complex Scenarios for Home Service Robot

Object Recognition and Grasp Detection in Complex Scenarios ...

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Chinese automation Congress (CAC)

作者： Shengjun Xu Liang Bai Huangwei Zhong Yuji Hu Xiangcai Sun Hongqiang Lyu School of Information and Control Engineering Xi'an University of Architecture and Technology Xi'an China School of Automation Science and Engineering Faculty of Electronic and Information Engineering Xi'an Jiaotong University Xi'an China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

With the development of artificial intelligence, home service robots have been widely applied in daily life. As one of the fundamental functions of service robots, object grasping helps improve the level of unmanned and intelligent household. However, traditional robots still face challenges in flexibility and accuracy in complex home environments. In this paper, an object recognition and grasp detection system in complex scenarios for home service robots is proposed. The system consists of four modules: image acquisition, human-machine interaction software, RGB-D image processing, and robot control. The image acquisition module captures RGB and depth images of the work area and sends them to the software. The RGB-D image processing module begins with object recognition to obtain object categories and bounding box with the help of YOLOv8. The target point cloud is produced by combining the target subimage and corresponding depth information, followed by depth completion and plane expansion. Then the processed point cloud is fed into a Graspnet pose estimation network to calculate the 6D grasping pose for service robot. And finally, the robot control module is invoked to carry out the corresponding target grasping action. To evaluate the performance, an experimental platform for home service robot is built to grasp various daily necessities. The results tell that the average accuracy for object recognition reaches up to 98.3%, the success rate for single-target grasping is 94.4%, and the success rate for multi-target grasping can achieve 80%.

关键词： Point cloud compression Human computer interaction Visualization Service robots Image processing Robot control Pose estimation Grasping Software Object recognition

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Exponential Weighted Moving Average-Based Variational Mode Decomposition Method for Fault Diagnosis of Vehicle Batteries 13

Exponential Weighted Moving Average-Based Variational Mode D...

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13th IEEE Data Driven control and Learning Systems Conference, DDCLS 2024

作者： Wu, Penghao Tao, Hongfeng Tian, Engang Yin, Tangwen Chen, Hongtian Chen, Yiyang School of Mechanical and Electrical Engineering Soochow University Suzhou215137 China Jiangnan University Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Wuxi214122 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Shanghai Jiao Tong University Department of Automation Shanghai200240 China

ISBN: (纸本)9798350361674

In recent years, the rapid advancement of electric vehicles has heightened concerns regarding the safety of high-energy batteries. Consequently, there has been a significant focus on the development of fault diagnosis technologies for batteries. Intelligent fault diagnosis (IFD), particularly data-driven approaches, represents a crucial methodology in addressing this concern. This paper proposes a data-driven approach, for IFD in high-energy batteries utilized in electric vehicles. The proposed Exponentially Weighted Average Variational Mode Decomposition (EWVMD) method overcomes these limitations by employing the exponential weighted moving average (EWMA) technique to smooth collected time-series voltage data. This process reduces noise, accentuates abnormal change trends, and yields secondary data with enhanced features. Subsequently, variational mode decomposition (VMD) is applied to the secondary data. The resulting decomposed sub-mode curves (intrinsic mode functions - IMFs) and the change trends of the residual curve, in conjunction with a fault threshold tailored to actual working conditions, are utilized to ascertain the occurrence of faults. Design verification experiments, the C42MSA electric vehicle power supply battery undergoes charging and discharging tests with injected faults at fixed intervals. The collected fault signals are then subjected to fault diagnosis using EWVMD. Results demonstrate the algorithm's superior detection effectiveness and increased sensitivity to fault signals compared to traditional methods. © 2024 IEEE.

关键词： Variational mode decomposition

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Deep Learning of Process Data with Supervised Variational Auto-encoder for Soft Sensor 11

Deep Learning of Process Data with Supervised Variational Au...

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11th IEEE Data Driven control and Learning Systems Conference, DDCLS 2022

作者： Tang, Xiaochu Yan, Jiawei Song, Zhihuan Zhang, Xinmin School of Automation Shenyang Aerospace University Liaoning Province Shenyang110136 China College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

Nonlinearity and uncertainty are two critical characteristics when process data modeling is applied to soft sensor. In this paper, a supervised variational auto-encoder (SVAE) is developed to capture both nonlinear and untcertain feature for regression modeling. SVAE, as a deep generative model, provides a probabilistic framework, based on which the deep nonlinear feature extraction is carried out and a probabilistic respresentation can be obtained. In this way, the probability distribution mapping between process data and key quality variables is learned so that quality prediction can be well achieved. The feasibility of the proposed method is illustrated by a numerical example and an industrial example, and the effectiveness of the proposed model is verified by comparing with the linear model. © 2022 IEEE.

关键词： Probability distributions

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Anti-disturbance control for Aerial-Recovery Drogue with Flexible Prescribed Performance

Anti-disturbance Control for Aerial-Recovery Drogue with Fle...

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International Conference on Guidance, Navigation and control, ICGNC 2024

作者： Zhu, Junfan Wang, Honglun Wang, Yanxiang Liu, Yiheng School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China The Science and Technology on Aircraft Control Laboratory Beihang University Beijing 100191 China Shenyuan Honors College Beihang University Beijing 100191 China Beijing Institute of Astronautical System Engineering Beijing 100076 China

ISBN: (纸本)9789819622115

The control of towed-cable-drogue system (TCDS) is important to achieving aerial recovery. In this paper, an adaptive prescribed performance control (APPC) based drogue controller is proposed to control drogue’s position while docking. First, the kinematic and dynamic models of controllable TCDS under multiple airflow disturbances are constructed. Second, the impact of command mutations and sudden disturbances may cause sudden increase of position tracking error and traditional PPC may encounter control singularity. To address the fragility of traditional PPC, an adaptive PPC is proposed. Combined with backstepping controller, the position controller is designed. Finally, the simulation results validate the effectiveness of the proposed method in addressing the control challenges of TCDS under command mutations and sudden disturbances. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Aerial recovery Prescribed performance control Towed-cable-drogue system

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Transient Stability Analysis of Grid-Connected Converter with Considering DC-Link Dynamics

Transient Stability Analysis of Grid-Connected Converter wit...

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2024 CPSS and IEEE International Symposium on Energy Storage and Conversion, CPSS and ISESC 2024

作者： Ling, Yangjian Huang, Meng Zha, Xiaoming Li, Weibing Jiang, Dingguo Li, Junxian School of Electrical Engineering and Automation Wuhan University Laboratory of Hydro-Wind-Solar Multi-energy Control Coordination Wuhan China Institute of Science and Technology China Three Gorges Corporation Beijing China Co. Ltd China Three Gorges Corporation Beijing China

ISBN: (纸本)9798350380514

Massive renewable energy is connected to the power grid though the grid-following (GFL) converter. The DC-link voltage fluctuates easily when experiencing the challenges, such as grid voltage disturbances, load fluctuation. However, many studies ignore the influence of the DC-link voltage dynamics on the GFL converter. In this paper, the dynamic active current caused by the DC-link voltage dynamics is analyzed for the transient stability of the GFL converter. The dynamic active current would introduce an extra negative damping term in the phase-locked loop (PLL). The estimated domain of attraction (DOA) is obtained though energy function and Lyapunov direct method. Finally, simulation results and experimental results verify the effectiveness of the theoretical analysis. © 2024 IEEE.

关键词： Transient stability

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