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63rd IEEE Conference on Decision and control, CDC 2024

作者： Wang, Jiwei Baldi, Simone Yu, Wenwu Yin, Xiang Southeast University School of Cyber Science and Engineering Nanjing210096 China Southeast University School of Mathematics Nanjing210096 China Shanghai Jiao Tong University Department of Automation Key Laboratory of System Control and Information Processing Shanghai200240 China

ISBN: (纸本)9798350316339

Fault diagnosis and prognosis in discrete event systems are studied in the scenario where the observations are possibly received with delay. To address this scenario, two conditions for diagnosis and prognosis with delayed observations are proposed, where we show that the state-of-the-art notion of prognosability must be revised to avoid conservativeness. Diagnosability and prognosability conditions are then verified by introducing a delay observer and a new verification function. Theoretical analysis indicates the effectiveness of the verification method for fault diagnosis and prognosis in the system. © 2024 IEEE.

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Multi-agent Deep Reinforcement Learning Based Multi-objective Charging control for Electric Vehicle Charging Station 7th

Multi-agent Deep Reinforcement Learning Based Multi-objectiv...

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7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Gong, Jiabao Fu, Weiming Kang, Yu Qin, Jiahu Xiao, Feng Department of Automation University of Science and Technology of China Hefei230027 China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering North China Electric Power University Beijing102206 China

ISBN: (纸本)9789819733279

This paper proposes a multi-agent deep reinforcement learning (MADRL) based algorithm for charging control of multiple electric vehicles (EVs) in an electric vehicle charging station (EVCS) with dynamic operations. By taking the uncertainties of EV users’ behaviors and charging prices into consideration, a Markov game with multiple objectives is first established, where the individual objective of reducing EV charging costs and the collective objective of mitigating charging load fluctuations are both studied. Then, a multi-agent actor-double-critic (MAADC) algorithm with a unified advantage actor-critic framework is proposed, where two advantage functions are designed as the local and global critics, and the integrated actor is employed to balance the learning towards the individual and collective objectives. The simulation results obtained in a delicately built EVCS environment show the effectiveness of MAADC. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Reinforcement learning

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Digital-Twin-Based Clock Synchronization in Industrial IoT systems 13

Digital-Twin-Based Clock Synchronization in Industrial IoT S...

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13th IEEE International Conference on CYBER Technology in automation, control, and Intelligent systems, CYBER 2023

作者： Cheng, Ting Liu, Shuai Xu, Qimin Zhu, Shanying Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China School of Control Science and Engineering Shandong University Jinan250061 China

ISBN: (纸本)9798350315196

Close and efficient cooperation between devices in the Industrial Internet of Things (IIoT) requires precise clock synchronization as a prerequisite. The uncertainty of IIoT networks and the complexity of industrial environments have led to less precise clock synchronization achieved by traditional algorithms and higher network resource consumption. This paper proposes a digital-twin-based clock synchronization scheme for IIoT systems. By building the digital model for each local clock to predict their clock skew, the information interaction required to achieve synchronization is reduced. In order to model the clock accurately, this paper proposes to use the extended state Kalman filter to handle uncertain dynamics and unknown measurement bias to improve the estimation accuracy of clock skew further. The simulation results show that the proposed algorithm can achieve high- precision clock synchronization while reducing the consumption of network resources. © 2023 IEEE.

关键词： Internet of things

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Adversarially Robust Reinforcement Learning for Urban Traffic Signal control 5

Adversarially Robust Reinforcement Learning for Urban Traffi...

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5th IEEE International Conference on Technology, Informatics, Management, engineering and Environment, TIME-E 2024

作者： Hamzah, Muhammad Shafly Joelianto, Endra Sutarto, Herman Yoseph Putri, Adiyana Institut Teknologi Bandung Instrumentation and Control Master Program Bandung Indonesia Institut Teknologi Bandung Instrumentation Control and Automation Research Group Bandung Indonesia PT. Pusat Riset Energi Department of Intelligent System Bandung Indonesia Institut Teknologi Bandung Doctoral Program of Engineering Physics Bandung Indonesia

ISBN: (纸本)9798350364743

Urban traffic congestion presents critical challenges, resulting in delays, environmental pollution, and significant economic costs. While traditional traffic control methods, such as fixed-time and adaptive systems, have been widely implemented, they often fail in unpredictable conditions, particularly when confronted with noisy or inaccurate data. This research proposes a robust reinforcement learning (RL) solution for traffic signal control using the RADIAL-DQN (Robust Adversarial Loss DQN) algorithm. Integrating adversarial training enhances the system's robustness to real-world uncertainties. Evaluations conducted using a SUMO traffic simulation show that the robust agent produced an increase of only 60.71% in queue length under a projected gradient descent (PGD) adversarial attack, outperforming standard RL agents, which experienced an 89% increase. These results demonstrate the potential of robust RL to improve both the reliability and scalability of urban traffic control systems. © 2024 IEEE.

关键词： Traffic signals

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Global Update Guided Federated Learning 41

Global Update Guided Federated Learning

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第41届中国控制会议

作者： Qilong Wu Lin Liu Shibei Xue Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China School of Electronic Information Engineering Shanghai Dianji University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Federated learning protects data privacy and security by exchanging models instead of ***,unbalanced data distributions among participating clients compromise the accuracy and convergence speed of federated learning *** alleviate this problem,unlike previous studies that limit the distance of updates for local models,we propose global-updateguided federated learning(FedGG),which introduces a model-cosine loss into local objective functions,so that local models can fit local data distributions under the guidance of update directions of global ***,considering that the update direction of a global model is informative in the early stage of training,we propose adaptive loss weights based on the update distances of local *** simulations show that,compared with other advanced algorithms,FedGG has a significant improvement on model convergence accuracies and ***,compared with traditional fixed loss weights,adaptive loss weights enable our algorithm to be more stable and easier to implement in practice.

关键词： Federated Learning Cosine Similarity Adaptive Loss Weights

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Data-driven Fault Diagnosis Method Based on Fusion of Multi-direction and Multi-source Signals 5

Data-driven Fault Diagnosis Method Based on Fusion of Multi-...

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5th International Conference on Intelligent Autonomous systems, ICoIAS 2022

作者： Yang, Yang Wang, Junda Li, Ning Shanghai Jiaotong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai China

ISBN: (数字)9781665498388

ISBN: (纸本)9781665498388

As a pivotal mechanical component of turbine drive-chain, gearbox suffers from various accidents and fault diagnosis could remarkably enhance its reliability and maintain running safety and lifetime. Vibration-based analysis on single-channel is mainly adopted in previous diagnostic, which cannot catch full vibration inner information in gearbox sensors and bring model performance down. In this paper, a novel method based on the fusion of multi-direction and multi-source signals and the convolutional neural network (MDMS-CNN) is presented. This method can obtain multi-dimensional feature maps from MDMS and preserve the time correlation among different signal sequences. First, features from MDMS are extracted via a corrosion conversion method that converts signals into two-dimensional (2-D) gray images. Then, homologous images are spliced into same channels, while heterogeneous images are superimposed to different channels. Finally, fusion features are input into CNN for fault classification. In experiments, the method is validated on a gearbox diagnosis case. Results have shown that the proposed method achieves 99.86% recognition accuracy and faster convergence speed compared to others. © 2022 IEEE.

关键词： Failure analysis

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Effective distributed algorithm for solving linear matrix equations

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Science China(Information Sciences) 2023年第8期66卷 265-266页

作者： Songsong CHENG Jinlong LEI Xianlin ZENG Yuan FAN Yiguang HONG Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and Automation Anhui University Department of Control Science and Engineering Tongji University Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology

Dear editor,Solving linear matrix equations is a basic and important problem in many fields such as the computation of generalized inverses of matrices and(generalized) Sylvester equations. Also, the linear algebraic ...

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STUDY ON RISK STATE IDENTIFICATION AND PREDICTION METHOD AND SOFTWARE TOOL DESIGN OF DIGITAL control systemS 30

STUDY ON RISK STATE IDENTIFICATION AND PREDICTION METHOD AND...

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30th International Conference on Nuclear engineering, ICONE 2023

作者： Zhang, Chao Chen, Sijuan Zhu, Ruoyu Sun, Jiajun Dong, Xiaomeng Li, Weihao Yang, Ming Xu, Anqi Wang, Jipu Department of Science and Technology Innovation State Nuclear Power Automation System Engineering Company Shanghai China Institute for Advanced Study in Nuclear Energy & Safety College of Physics and Optoelectronic Engineering Shenzhen University Guangdong Province Shenzhen City China Power Plant Control Center State Nuclear Power Automation System Engineering Company Shanghai China Shenzhen University Guangdong Province Shenzhen City China

ISBN: (纸本)9784888982566

With the overall improvement of the digital and intelligent level in the nuclear power industry, the failure of the Digital control system (DCS) has become one of the major factors affecting the stable operation of nuclear power units. DCS safety problems can easily lead to the unit outage or shutdown, power reduction operation, system or equipment degradation events and other serious consequences, which will significantly threaten the safety and reliability of the unit operation. This paper studies the real-time safety problem of DCS based on the monitoring data of DCS itself, focusing on DCS current operating risk state identification, operating risk state transition model construction, residual operating risk prediction, to evaluate DCS operating risk state and predict the risk evolution trend. Furthermore, this study focus on the design and development of the software tool for risk state identification and prediction of DCS, so as to implement the methods and algorithms presented herein. © 2023 by JSME.

关键词： Digital control systems

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Multi-Channel Based on Attention Network for Infrared Small Target Detection

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Journal of Shanghai Jiaotong university(Science) 2024年第3期29卷 414-427页

作者：张彦军王碧云蔡云泽 Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China Shanghai Institute of Aerospace Control Technology Shanghai 201109China Infrared Detection Technology R&D Center of China Aerospace Science and Technology Corporation Shanghai 201109China Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai 200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong UniversityShanghai 200240China Institute of Marine Equipment Shanghai Jiao Tong UniversityShanghai 200240China

Infrared detection technology has the advantages of all-weather detection and good concealment,which is widely used in long-distance target detection and tracking ***,the complex background,the strong noise,and the characteristics of small scale and weak intensity of targets bring great difficulties to the detection of infrared small targets.A multi-channel based on attention network is proposed in this paper,aimed at the problem of high missed detection rate and false alarm rate of traditional algorithms and the problem of large model,high complexity and poor detection performance of deep learning ***,given the difficulty in extracting the features of infrared multiscale and small dim targets,the multiple channels are designed based on dilated convolution to capture multiscale target ***,the coordinate attention block is incorporated in each channel to suppress background clutters adaptively and enhance target *** addition,the fusion of shallow detail features and deep abstract semantic features is realized by synthesizing the contextual attention fusion ***,it is verified that,compared with other state-of-the-art methods based on the datasets SIRST and MDFA,the proposed algorithm further improves the detection effect,and the model size and computational complexity are smaller.

关键词： infrared image small target detection deep learning attention mechanism feature fusion

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A VIO-Based Localization Approach in GPS-denied Environments for an Unmanned Surface Vehicle 8

A VIO-Based Localization Approach in GPS-denied Environments...

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8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Liu, Xinze Hu, Zhihuan Sun, Zhijian Lu, Jinhui Xie, Wei Zhang, Weidong Shanghai Jiao Tong University Department of Automation Shanghai200240 China Crossocean of Suzhou Technology No. 218 East Qingdao Road Suzhou China National Engineering Research Center of Ship & Shipping Control System

ISBN: (纸本)9798350300178

In this paper, we propose a visual inertial odometer (VIO)-based localization approach for USV in GPS-denied environments. Considering the difficulty in capturing reliable features on the water surface, we set the camera's pitch angle properly to capture high-quality features, including points and lines, distributed on the bridge wall. Our work is based on the state-of-the-art simultaneous localization and mapping (SLAM) framework, VINS-Fusion. The point and line features observed by the left and right cameras are both added into the nonlinear optimization system along with the inertial measurement unit (IMU) measurement between frames. Through the joint optimization of landmark information and system state variables, the precise positioning of USV is realized. To validate the effectiveness of the proposed localization method, experimental results are presented and analyzed. © 2023 IEEE.

关键词： Cameras

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