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Model Reference Adaptive Stabilizing control for Leader-following Consensus

IEEE Transactions on Automatic control 2025年

作者： Yue, Dongdong Shi, Jiantao Shi, Ling Frasca, Paolo Baldi, Simone Nanjing Tech University College of Electrical Engineering and Control Science Nanjing211816 China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Clear Water Bay China Univ. Grenoble Alpes Cnrs Inria Grenoble Inp GIPSALab Grenoble38000 France Southeast University School of Mathematics Nanjing210096 China

Complex networks, neuroscience, and other applications have shown examples of multi-agent adaptive systems that must follow (over possibly short times) reference dynamics that are neither Hurwitz nor neutrally stable. However, such leader-following behavior would be impossible with existing adaptive consensus methods, e.g., based on model reference adaptive control (MRAC), since the stability of the reference dynamics is required. To fill this gap, we propose a novel model reference adaptive stabilizing control (MRASC) framework for leader-following consensus of multi-agent systems with unknown and heterogeneous dynamics. Differently from several approaches in the leader-following consensus literature, the proposed framework is free of any extra distributed observer layer for the leader's signal, as the reconstruction of such signals is intrinsic in the adaptive laws. Besides, the framework does not require Hurwitz or neutral stability of the leader and generalizes existing acyclic requirements on the communication graph among the follower. Starting from any weakly connected communication digraph, the proposed method allows to derive a lower bound, useful from the network design point of view, for the minimum number of followers that should be pinned by the leader. © 2025 IEEE.

关键词： Multi agent systems

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Wi-Wheat+: Contact-free wheat moisture sensing with commodity WiFi based on entropy

Wi-Wheat+: Contact-free wheat moisture sensing with commodit...

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作者： Yang, Weidong Shen, Erbo Wang, Xuyu Mao, Shiwen Gong, Yuehong Hu, Pengming College of Information Science and Engineering Henan University of Technology Zhengzhou450001 China Henan Key Laboratory of Grain Photoelectric Detection and Control Zhengzhou450001 China College of International Education Kaifeng University Henan Kaifeng475004 China Knight Foundation School of Computing & Information Sciences Florida International University MiamiFL33199 United States Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

In this paper, we propose a contact-free wheat moisture monitoring system, termed Wi-Wheat+, to address the several limitations of the existing grain moisture detection technologies, such as time-consuming process, expensive equipment, low accuracy, and difficulty in real-time monitoring. The proposed system is based on Commodity WiFi and is easy to deploy. Leveraging WiFi CSI data, this paper proposes a feature extraction method based on multi-scale and multi-channel entropy. The feasibility and stability of the system are validated through experiments in both Line-Of-Sight (LOS) and Non-Line-Of-Sight (NLOS) scenarios, where ten types of wheat moisture content are tested using multi-class Support Vector Machine (SVM). Compared with the Wi-Wheat system proposed in our prior work, Wi-Wheat+ has higher efficiency, requiring only a simple training process, and can sense more wheat moisture content levels. © 2022 Chongqing University of Posts and Telecommunications

关键词： Channel state information (CSI) Multi-class support vector machine (SVM) Multi-scale entropy Radio frequency (RF) sensing WiFi

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Path planning algorithm for unmanned surface vessels based on COLREGs and Meta-TD3

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Ocean engineering 2025年 334卷

作者： Sun, Xu Li, Gangsheng Liu, Zhaokai Zhang, Ling Yu, Haibo Song, Dalei Wu, Q.M. Jonathan College of Engineering Ocean University of China Qingdao266500 China Department of Education Ocean University of China Qingdao266100 China Department of Statistics and Data Science Southern University of Science and Technology Shenzhen518055 China Fundamental Computer Department Ocean University of China Qingdao266100 China Department of Electrical and Computer Engineering University of Windsor WindsorONN9B3P4 Canada School of Control Science and Engineering Shandong University Jinan250061 China

—Unmanned surface vessels (USVs) may encounter unknown obstacles, moving vessels and the influence of sea current while at sea. How to make path planning of USVs under so many complex factors is significant for maritime traffic safety and executing task efficiently. To solve this challenging problem, an improved adaptive interfered fluid dynamic system algorithm is proposed in this paper. The method first designs a corresponding obstacle avoidance strategy according to the International Regulations for Preventing Collisions at Sea (COLREGs), which enables the vessel to perform path planning according to the COLREGs, and introduces simulated sea currents for energy-efficient path planning. Then, a Kalman filtering method is integrated into the algorithm to predict dynamic obstacle trajectories. The Twin Delay Depth Deterministic Strategy Gradient (TD3) is also improved by combining it with meta-learning to increase the adaptability of the method to different environmental conditions. In addition, the reward function is designed to take COLREGs and the sea current into account so that the USV can safely avoid collisions and save energy as much as possible. The experimental results demonstrate that the proposed path planning method can avoid moving obstacles according to COLREGs and can utilize sea currents for energy-saving path planning. © 2025 Elsevier Ltd

关键词： Highway accidents

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Test Case Generation for Ethereum Smart Contract based on Data Dependency Analysis of State Variable

Test Case Generation for Ethereum Smart Contract based on Da...

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IEEE International Conference on Software Quality, Reliability and Security (QRS)

作者： Jinhu Du Song Huang Xingya Wang Changyou Zheng Jinlei Sun Command & Control Engineering College Army Engineering University of PLA Nanjing China College of Computer Science and Technology Nanjing Tech University Nanjing China

An Ethereum smart contract is an agreement reached by multiple parties, which is guaranteed by blockchain technology to be executed in accordance with the terms expressed in the form of code. Its security needs are particularly prominent due to a large number of digital assets under management. Testing is an effective way to find flaws that threaten the security of smart contracts. However, current smart contract test case generation methods do not regard the impact of other functions in the smart contract on state variables, resulting in the inaccessibility of the control statements related to state variables and low branch coverage of the function under test. To alleviate this problem, this paper proposes SV-Gen. SV-Gen generates test cases for smart contracts through two steps: static analysis and dynamic search. In the first step, SV-Gen considers the read-write relationship between functions and state variables in the smart contract to generate a function invocation sequence for the function to be tested through a backtracking algorithm on state variables. Then the arguments of transactions to invoke each function in the sequence are generated through regex matching to form the primitive test case. In the second step, the primitive test cases constitute an initial population, and a genetic algorithm undertakes the task of evolving them to high branch coverage. The experimental results on one of the VeriSmart datasets show that SV-Gen can effectively enter the control constraints related to state variables and improve the branch coverage of smart contracts.

关键词： Smart contracts Software algorithms Sociology Static analysis Software quality Software reliability Security

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Tangentially-Magnetized-Slot-PM-Assisted Magnet Saturation Relieving Design for Doubly Salient Reluctance Machine Position Sensorless Drive Electric Propulsion Applications

Tangentially-Magnetized-Slot-PM-Assisted Magnet Saturation R...

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Electrical engineering and control Technologies (CEECT), Conference on

作者： Weiyu Wang Xiaodong Zhang Shuangxia Niu Xing Zhao Jianfei Pan Weinong Fu School of Engineering Great Bay University Dongguan China Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University Hong Kong China Department of Electronic Engineering University of York York United Kingdom College of Mechatronics and Control Engineering Shenzhen University Shenzhen China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China

ISBN: (数字)9798331528096

ISBN: (纸本)9798331528102

For doubly salient reluctance machines, the magnetic saturation effect easily occurs, thereby the saturated inductance with complex analytical expression brings difficulties to the inductance modeling procedure during position sensorless control. To tackle this issue, a direct solution from electrical machine design is introduced to regulate magnetic flux distribution. The key is to insert tangentially-magnetized-slot-permanent-magnet into stator slots, thus relieving the magnetic saturation in the stator teeth. Consequently, unsaturated inductance characteristics can be acquired through the magnetic saturation relieving effect, thereby the inductance modeling effort can be significantly decreased, and such design is friendly for doubly salient reluctance machine position sensorless drive application.

关键词： Magnetic flux Inductance Analytical models Windings Stator windings Sensorless control Reluctance machines Magnetic analysis Saturation magnetization Load modeling

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Polyp Segmentation Neural Network with Simple Encoding Structure and Reverse Attention Module

Polyp Segmentation Neural Network with Simple Encoding Struc...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Jie Wang Zuoyong Li Haiping Xu Xuesong Cheng Shenghua Teng College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China Fujian Key Laboratory of Medical Big Data Engineering Fujian Provincial Hospital Fuzhou China College of Mathematics and Data Science Minjiang University Fuzhou China

Colorectal cancer is a common malignancy. In colonoscopy images, computer-assisted polyp segmentation helps doctors diagnose and treat disorders more precisely. In recent years, some methods based on deep convolutional neural networks have made significant breakthroughs in this task. However, only a few algorithms explicitly consider the effect of light intensity on segmentation performance, which results in algorithms needing to be more robust for complex samples. This paper proposes a neural network combining a simple encoding structure with a reverse attention module. Specifically, the model uses HarDNet68 as the backbone network, adds a camouflage identification module (CIM) to enhance the capture of polyp details on the underlying features, and finally utilizes adaptive feature fusion with the reverse attention module (RAM). The proposed model effectively addresses the challenge of poor segmentation quality in colonoscopy images caused by inconsistent illumination. Extensive experiments on five datasets show that the proposed model performs well in polyp segmentation.

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Energy Management Strategy for Fuel Cell Hybrid Emergency Power System based on Deep Reinforcement Learning

Energy Management Strategy for Fuel Cell Hybrid Emergency Po...

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AI (ISAI), International Symposium on

作者： Jingyi Xi Xiaoyin Hu Duo Xiao College of Control science and Engineering Zhejiang University Hangzhou China School of Information & Electrical Engineering Hangzhou City University Hangzhou China School of Computer & Computing Science Hangzhou City University Hangzhou China The academy of Edge Intelligence Hangzhou City University Hangzhou China

ISBN: (数字)9798350389074

ISBN: (纸本)9798350389081

Addressing the challenges in energy consumption and lifespan of fuel cell hybrid emergency power system, this paper proposes an energy management strategy based on deep reinforcement learning. The strategy aims to holistically balance objectives such as hydrogen consumption of the fuel cell, its lifespan, the stability of the lithium battery’s state of charge (SOC), and its degradation. Firstly, we present the system architecture of the fuel cell hybrid emergency power system and establish models for hydrogen consumption and degradation of the fuel cell, as well as the SOC variations and degradation of the lithium battery, upon which a multi-objective reward function is constructed. Subsequently, we adopt the distributional soft actor-critic (DSAC) deep reinforcement learning algorithm, combined with a maximum entropy-based objective function to balance the exploration-exploitation trade-off and incorporated with a distributed value function estimation to alleviate the overestimation of Q-values. Simulation results demonstrate that the proposed strategy can effectively reduce the overall value loss, extend the system’s lifespan, and showcase robust adaptability.

关键词： Degradation Hydrogen Fuel cells Power system stability Deep reinforcement learning Linear programming Hybrid power systems Stability analysis State of charge Energy management

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Bias-Eliminated Semantic Refinement for Any-Shot Learning

arXiv

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arXiv 2022年

作者： Feng, Liangjun Zhao, Chunhui Li, Xi The State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China The College of Computer Science and Technology Zhejiang University Hangzhou310027 China

When training samples are scarce, the semantic embedding technique, i.e., describing class labels with attributes, provides a condition to generate visual features for unseen objects by transferring the knowledge from seen objects. However, semantic descriptions are usually obtained in an external paradigm, such as manual annotation, resulting in weak consistency between descriptions and visual features. In this paper, we refine the coarse-grained semantic description for any-shot learning tasks, i.e., zero-shot learning (ZSL), generalized zero-shot learning (GZSL), and few-shot learning (FSL). A new model, namely, the semantic refinement Wasserstein generative adversarial network (SRWGAN) model, is designed with the proposed multihead representation and hierarchical alignment techniques. Unlike conventional methods, semantic refinement is performed with the aim of identifying a bias-eliminated condition for disjoint-class feature generation and is applicable in both inductive and transductive settings. We extensively evaluate model performance on six benchmark datasets and observe state-of-the-art results for any-shot learning;e.g., we obtain 70.2% harmonic accuracy for the Caltech UCSD Birds (CUB) dataset and 82.2% harmonic accuracy for the Oxford Flowers (FLO) dataset in the standard GZSL setting. Various visualizations are also provided to show the bias-eliminated generation of SRWGAN. Our code is available. Copyright © 2022, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Stability Analysis of Connected Vehicle Platoons with Markovian Packet Losses

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IEEE Internet of Things Journal 2025年

作者： Wu, Yilin Zhao, Xiaoyan Shen, Zhiping Liu, Yonggui Li, Zeming Guangdong University of Education School of Computer Science Guangzhou510310 China Henan Normal University School of Mathematics and Statistics Xinxiang453007 China South China University of Technology Key Laboratory of Autonomous Systems and Network Control Ministry of Education The College of Automatic Science and Engineering Guangzhou510640 China

This article focuses on discussing the effects of inter-vehicular communication on the performance of platoon control. Different from the existing results established under a critical assumption that the fading channels are independent, this paper models the inter-vehicular communication as Markovian packet loss channel which can capture the temporal interdependencies of packet losses. Our aim is to decide whether there exists a distributed controller such that the underlying vehicle platoon can achieve mean square stability (MSS) over Markovian packet loss channels. First, for the scenario with identical Markovian packet losses, we begin by deriving a necessary and sufficient condition for the platoon MSS, which is established by utilizing the stability principles of Markov jump linear systems. Additionally, we propose a numerically verifiable criterion for MSS, which can be validated through the feasibility of linear matrix inequalities (LMIs). Furthermore, based on the modified Riccati inequality (MRI), we provide an analytical sufficient condition that ensures MSS for vehicle platoons. This condition is expressed in terms of the information flow topology (IFT) and the statistical parameters of Markov channels. Second, for the case with nonidentical packet dropouts, we introduce a Markovian loss channel based switching (MLCBS) model. Building upon this model, we further propose a sufficient conditon related to IFT and the transition probability matrix, and a distributed controller that is not only easy to implement but also enables the achievement of MSS for vehicle platoons with nonidentical packet dropouts. Ultimately, numerical experiments are performed to validate the obtained outcomes. © 2014 IEEE.

关键词： Intelligent Transportation Systems Resource-Constrained Networks Vehicular Networks

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Contrastive Conditional Adversarial Autoencoder with Class-specific Forces for Imbalanced Open-set Fault Detection

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IEEE Transactions on Automation science and engineering 2025年 22卷 15757-15767页

作者： Zuo, Zhonglin Meng, Tao Li, Zheng Zhang, Hao Liu, Tong Chen, Zhansheng Pang, Zhibo Zhejiang University College of Control Science and Engineering Hangzhou310027 China Zhejiang University School of Aeronautics and Astronautics Hangzhou310027 China Hainan Institute of Zhejiang University Sanya572025 China Chongqing University School of Automation Chongqing400044 China University of Sheffield Department of Computer Science SheffieldS1 4DP United Kingdom Shanghai Aerospace Space Technology Co. Ltd Shanghai201109 China ABB Corporate Research Sweden Department of Automation Technology Vasteras Sweden KTH Royal Institute of Technology Department of Intelligent Systems Stockholm Sweden

In real-world industrial scenarios, fault detection faces the widely recognized challenge of data imbalance, which not only refers to the scarcity of fault data but also includes the imbalance in healthy data. This article is concerned with imbalanced open-set fault detection (IOSFD), a practical yet challenging scenario in industrial applications where multiple healthy operating conditions and multiple fault types are imbalanced. In this article, we propose a new contrastive conditional adversarial autoencoder for IOSFD. It constructs an end-to-end unified model based on multi-class known healthy and faulty data to address the reliance of traditional methods on fault samples, while optimizing with class-specific weighted forces to ensure equal attention to imbalanced known classes. Input and feature reconstruction conditioned on operating modes are utilized to learn a compact decision plane and achieve both unknown fault detection and known data classification. Significantly, we formulate the optimization objective of conditional reconstruction based on contrastive learning and introduce adversarial training to further enhance the model’s performance. The effectiveness of the proposed method is validated through real-world pipeline leak detection and Tennessee-Eastman multi-fault detection. © 2004-2012 IEEE.

关键词： Contrastive Learning

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