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Reinforcement Learning-Based Fault-Tolerant control of Uncertain Strict-Feedback Nonlinear Systems With Intermittent Actuator Faults

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IEEE Transactions on Neural Networks and Learning Systems 2025年第6期36卷 10464-10478页

作者： Qinmin Yang Huaying Li Zhengwei Ruan Bo Fan Shuzhi Sam Ge State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China Hangzhou Huawei Communication Technology Company Ltd. Hangzhou China School of Automation Science and Engineering Xi’an Jiaotong University Xi’an China Department of Electrical and Computer Engineering Institute for Functional Intelligent Materials National University of Singapore Queenstown Singapore

In this work, a novel reinforcement learning-based adaptive fault-tolerant control (FTC) scheme with actuator redundancy is presented for a nonlinear strict-feedback system with nonlinear dynamics and uncertainties. A learning-based switching function technique is established to steer different groups of actuators automatically and successively to mitigate the impact of faulty actuators by observing a switching performance index. The optimal tracking control problem (OTCP) of strict-feedback nonlinear systems is transformed into an equivalent optimal regulation problem of each affine subsystem via adaptive feedforward controllers. Subsequently, the designed objective functions associated with Hamilton–Jacobi–Bellman (HJB) estimate errors caused by neural network (NN) approximations can be minimized by the reinforcement learning algorithm without value or policy iterations. It is proved that the tracking objective can be achieved and all signals in the closed-loop system can be guaranteed to be bounded, as long as the minimum time interval between two successive failures is bounded. Theoretical results are verified by simulations.

关键词： Actuators Switches Fault tolerant systems control systems Optimal control Redundancy Nonlinear dynamical systems Adaptive control Regulation Industrial control

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Real-Time Routing Design for LEO Satellite Networks: An Enhanced Multi-Agent DRL Approach

Real-Time Routing Design for LEO Satellite Networks: An Enha...

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IEEE International Conference on communications in China Workshops (ICCC)

作者： Hailong Liao Xian Zhang Jiaen Zhou Xuehua Li Key Laboratory of Information and Communication Systems Ministry of Information Industry and the Key Laboratory of Modern Measurement & Control Technology School of Information and Communication Engineering Beijing Information Science and Technology University Beijing China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350377675

ISBN: (纸本)9798350377682

Low Earth Orbit (LEO) satellite networks have been heralded as a promising solution for provisioning space-based Internet services. However, the inherent dynamic topology, characterized by frequent variations in inter-satellite link states, causes pronounced fluctuations in latency and exacerbated packet loss rates. To address the challenge, in this paper, we formulate a routing design and optimization problem to maximize the average throughput with considering the end-to-end latency constraint and the real-time routing demand in LEO satellite networks. To solve this problem, we design an enhanced Multi-Agent Deep Reinforcement Learning (MADRL) algorithm, which encapsulates the intricate interplay among LEO satellite agents and their environs within a Real-Time Markov Decision Process (RTMDP) framework, and introduces the self-attention mech-anism to endow agents with a better grasp of environmental state dynamics. Simulation results demonstrate that our proposed routing design outperforms the benchmark schemes in terms of both end-to-end latency and network average throughput.

关键词： Satellites Network topology Simulation Heuristic algorithms Low earth orbit satellites Routing Throughput Real-time systems Topology Optimization

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A Multi-Scale SAR-Optical Image Matching Method Using Structure-Enhanced Convolutional Layer and Transformer-CNN Model

A Multi-Scale SAR-Optical Image Matching Method Using Struct...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Yijun Liu Jie Long Qian Liu Chang Liu Qingsong Wang School of Electronics and Communication Engineering Shenzhen Campus of Sun Yatsen University Shenzhen China Beijing Institute of Control and Electronic Technology Beijing China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Due to the geometric and nonlinear radiometric differences (NRD) between SAR and optical images, coupled with the impact of speckle noise, achieving robust and high-precision matching between them remains challenging. This paper introduces a novel multi-scale SAR-optical image matching method, which employs an improved multi-scale matching strategy. Specifically, we first establish an image pyramid and detect geometric keypoints of the images, subsequently segmenting the images into sub-blocks centered around these keypoints. These sub-blocks are then fed into the proposed siamese feature description network, comprising of a structure-enhanced convolutional layer and a Transformer-CNN model, for feature descriptors extraction, followed by feature matching. Finally, the matching point pairs are obtained by combining the matching relationships between image pairs of different scales. Experimental results show that our proposed algorithm achieves better matching performance compared to the existing state-of-the-art algorithms.

关键词： Image matching Speckle Optical fiber networks Feature extraction Transformers Optical imaging Radiometry

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Defense of Signal Modulation Classification Attack Based on GAN 7th

Defense of Signal Modulation Classification Attack Based on ...

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7th China National Conference on Big Data and Social Computing, BDSC 2022

作者： Zhu, Jiawei Liu, Yuhang Xu, Dongwei Yang, Hao Xuan, Qi Wang, Shunling Science and Technology on Communication Information Security Control Laboratory Jiaxing314033 China Institute of Cyberspace Security Zhejiang University of Technology Hangzhou310023 China College of Information Engineering Zhejiang University of Technology Hangzhou310023 China

ISBN: (纸本)9789811975318

Although deep learning has been broadly used in all kinds of domains and has also achieved very good performance, it is obvious that deep learning is very vulnerable to be attacked shown by extensive researches (e.g. even a very small perturbation is added to the raw signal data, the result of the modulation classification of the signal by the deep neural network will be wrong), which will cause serious consequences. Thus, it is necessary to find some ways to defense this attack. As a new deep learning model in recent years, Generative Adversarial Network (GAN) has been applied to the field of radio communication, which can encrypt the signal data, so as to ensure the reliable transmission of the signal. In this paper, a defense method against signal modulation classification attack based on GAN is developed, which is used to utilize generator to reconstruct the signal sample to improve the classification accuracy of the model, while the defense ability of the model against adversarial samples is improved. It has been shown that the method achieved good results in experiments. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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Potential Transmission Choice for Internet of Things(IoT):Wireless and Batteryless communications and Open Problems

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China communications 2021年第2期18卷 241-249页

作者： Zhan Xu Guanjie Hu Minzheng Jia Lan Dong School of Information and Communication Engineering and with Key Laboratory of Modern Measurement&Control Technology Ministry of EducationBeijing Information Science&Technology UniversityChina Beijing Key Lab of Transportation Data Analysis and Mining School of Computer and Information TechnologyBeijing Jiaotong UniversityBeijingChina Department of Information Engineering Beijing Polytechnic CollegeBeijingChina

The 5th generation mobile communications aims at connecting everything and future Internet of Things(IoT)will get everything smartly *** realize it,there exist many *** key challenge is the battery problem for small devices,such as sensors or *** backscatter,also referred to as or battery-free backscatter,is a new potential technology to address this *** early and typical type of batteryless backscatter is ambient ***,batteryless backscatter utilizes environmental wireless signals to enable battery-free devices to communicate with each *** devices first harvest energy from ambient wireless signals and then backscatter these signals so as to transmit their own *** paper reviews the current studies about batteryless backscatter,including various backscatter schemes and theoretical works,and then introduces open problems for future research.

关键词： batteryless backscatter battery-free channel state information(CSI) channel estimation multiple antennas signal detection symbiotic communication

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Target Detecting and Target Tracking Based on YOLO and Deep SORT Algorithm 4th

Target Detecting and Target Tracking Based on YOLO and Deep ...

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4th EAI International Conference on 6G for Future Wireless Networks, 6GN 2021

作者： Zhen, Jialing Ye, Liang Li, Zhe Department of Information and Communication Engineering Harbin Institute of Technology Harbin150080 China Health and Wellness Measurement Research Group OPEM Unit University of Oulu Oulu90014 Finland China Academy of Launch Vehicle Technology Beijing100076 China

ISBN: (纸本)9783031042447

The realization of the 5G/6G network can ensure high-speed data transmission, which makes it possible to realize high-speed data transmission in the monitoring video system. With the technical support of 5G/6G, the peak transmission rate can reach 10G bit/s, which solves the problems of video blur and low transmission rate in the monitoring system, and provides faster and higher resolution monitoring pictures and data, and provides a good condition for surveillance video target tracking based on 5G/6G network. In this context, based on the surveillance video in the 5G/6G network, this paper implements a two-stage processing algorithm to complete the tracking task, which solves the problem of target loss and occlusion. In the first stage, we use the Yolo V5s algorithm to detect the target and transfer the detection data to the Deep SORT algorithm in the second stage as the input of Kalman Filter, Then, the deep convolution network is used to extract the features of the detection frame, and then compared with the previously saved features to determine whether it is the same target. Due to the combination of appearance information, the algorithm can continuously track the occluded objects;The algorithm can achieve the real-time effect on the processing of surveillance video and has practical value in the future 5G/6G video surveillance network. © 2022, ICST institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Target tracking

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Autonomous Localization Method for Railway Trains Based on Multi-Source Information Fusion

Autonomous Localization Method for Railway Trains Based on M...

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Electronic engineering and Information Systems (EEISS), International Conference on

作者： Hanxuan Zhang Ju Huo Muyao Xue Jianbao Zhou School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China Shanghai Space Propulsion Technology Research Institute Shanghai China Harbin Nuoxin University of Technology Measurement and Control Technology Co. Ltd. Harbin China

ISBN: (数字)9798350351033

ISBN: (纸本)9798350351040

Aiming at the problems of high cost and difficult maintenance in the practical application of the traditional "vehicle-ground cooperation" train positioning technology in special section railway projects. In this paper, a method for autonomous positioning of railway trains based on multisource information fusion is proposed. First, develop a kilometer mark detection thread based on VINS Fusion, and build a lightweight optical symbol recognition system to identify the character area of the kilometer mark to obtain the position information of the electronic map. Secondly, based on the LSD line detection algorithm combined with the way of dynamically expanding the line, the coordinates of the vertex coordinates of the kilometer mark are extracted. Then, a global optimization objective function containing the position information constraints of the kilometer mark is established, and the global positioning accuracy is improved through the graph optimization method. This solves the problem that existing intelligent positioning of railway trains cannot eliminate accumulated errors in the absence of loop closure detection. Finally, the semi-physical simulation results show that the positioning error of the original positioning system can be reduced by more than 57%.

关键词： Location awareness Costs Simulation Symbols Optimization methods Linear programming Rail transportation

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Mathematical Modeling of the Impact of a 5 Day Interim Relaxation at 21 Days in 42 days Lockdown Strategy on COVID-19 Transmission in India 10

Mathematical Modeling of the Impact of a 5 Day Interim Relax...

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10th International Conference on Biosignals, Images and Instrumentation, ICBSII 2024

作者： Immaculyne, Flavia Bakiyaambikapathy Krishnamurthy, Kamalanand De Britto, Lourduraj Benoi Cresent Schlumberger- Reda Production System Singapore Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Anna University Department of Instrumentation Engineering MIT Campus Chennai India Vector Control Research Centre Indian Council of Medical Research Puducherry India

ISBN: (纸本)9798350350951

As of the first week of May 2020, the COVID-19 pandemic is present in 212 nations globally. Many countries have enforced lockdown measures strategically to slow down the epidemic's spread, with the objective of relieving pressure on healthcare systems. Mathematical modelling serves as a useful tool for the prediction of the future numbers of the infected population and for analyzing the effects of various intervention strategies. In this work, a mathematical model based on ordinary differential equations has been developed for analyzing the impact of an interim relaxation of 5 days at 21 days in a 42 days lockdown period. Results of the simulation demonstrate that the relaxation of 5 days during the 42 days lockdown does not result in any adverse effects if the transmission rate remains the same. However, the model-based analysis suggests that an increase in the transmission rate by a factor of 1.2 times during the relaxation period due to the possible aggregation of the susceptible with the infected, it could shift the growth pattern of infected cases from sub-exponential to exponential. © 2024 IEEE.

关键词： COVID-19

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Data Security Risk Assessment Method of Intelligent and Connected Vehicles Based on Data Security Classification and Grading 8

Data Security Risk Assessment Method of Intelligent and Conn...

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8th IEEE International Conference on Intelligent Transportation engineering, ICITE 2023

作者： Wang, Liyong Jin, Long Ji, Haojie Wang, Jingyan Chen, Yupeng Fang, Junzhe State Key Laboratory of Intelligent Vehicle Safety Technology Chongqing China Ministry of Education Beijing Information Science & Technology University Key Laboratory of Modern Measurement & Control Technology Beijing China State Key Laboratory of Intelligent Vehicle Safety Chongqing China China Automotive Engineering Research Institute Co. Ltd Chongqing China

ISBN: (纸本)9798350314212

The rapid development of intelligent and connected vehicle (ICV) has brought increasing communication data and valuable data assets. However, the diversity and complexity of ICV data make it hard to carry out data security risk assessment. Aiming at the problem, this paper proposes an effective analytical method for the data security risk assessment method of ICVs. Considering the typical scenario for the generated data of ICVs, a data security classification and grading framework is constructed from the aspects of in-vehicle data, personal data, and external environment data. The data security risk assessment model is built for ICVs and includes functional modules such as asset identification, threat identification, impact assessment, vulnerability identification, and risk assessment. This work provides methods and security management and control strategy support for ICV data security risk assessment. © 2023 IEEE.

关键词： data classification and grading data security Intelligent and connected vehicles risk assessment

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A Time-Efficient Path Navigating Landmine Detection Robot

A Time-Efficient Path Navigating Landmine Detection Robot

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Intelligent Signal Processing and Effective communication Technologies (INSPECT), IEEE International Conference on

作者： Rituraj Bhattacharjee Subhradip Mukherjee Hardik Gururaj Manish Rajpurohit Instrumentation & Control Engineering Department Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Electronics and communication Department MVJ College of Engineering Bangalore India

ISBN: (数字)9798350379525

ISBN: (纸本)9798350379532

The goal of a landmine detection robot is to map the remaining area on a visual map with millimeter precision after covering as much ground as it can show landmines. The prototype land mine detection robot model described in this study offers a visual interface for mapping landmines, modifying PIDs, and aligning cameras. It is also reasonably powerful, accurate, and easy to operate with other sensors. The manual, semi-auto, and auto modes of controlling the differential drive robot are emphasized. Image processing determines the exact location of the robot and provides live reckoning feedback to its dead reckoning servo control. A beat metal detector is a kind of quiet sensor used to locate landmines. The overall objective of the system is to provide the user with something robust, affordable, and easily comprehensible for the user.

关键词： Visualization Landmine detection Robot vision systems Prototypes Metals Detectors Manuals Signal processing Servosystems Intelligent sensors

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