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Distributed Estimation and Motion Control in Multi-Agent Systems under Multiple Attacks

IEEE Transactions on Automation Science and Engineering 2025年 22卷 12548-12559页

作者： Jenabzadeh, Ahmadreza Shu, Zhan Huang, Tingwen Zhu, Quanmin Shang, Yilun Cui, Yukang Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Peng Cheng Laboratory Shenzhen518000 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China University of the West of England School of Engineering BristolBS16 1QY United Kingdom Northumbria University Faculty of Engineering and Environment Department of Computer and Information Sciences Newcastle upon TyneNE1 8ST United Kingdom

This paper addresses the problem of distributed estimation and motion control (DEMC) in multi-agent systems (MASs) with both linear and Lipschitz nonlinear dynamics. Unlike conventional DEMC methods designed for MASs under ideal conditions, this work investigates scenarios where all agents are vulnerable to various forms of attacks. The considered attacks comprise false-data injection (FDI) attacks and denial of service (DoS) attacks that affect the communication channels among agents to destabilize the MAS. Also, the unbounded actuator attacks which exist in practical environments to intentionally degrade the MAS performance is considered. To cope with these kinds of attacks, two novel resilient approaches are established aimed at estimating and following a mobile target under attacks. The proposed distributed attack-resilient control strategies are designed based on a dual-layer structure, guaranteeing effective DEMC with an ultimately bounded error. The results from two simulation examples are provided to validate the presented algorithms. © 2004-2012 IEEE.

关键词： Multi agent systems

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A Lyapunov-Based Framework for Trajectory Planning of Wheeled Vehicle Using Imitation Learning

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IEEE Transactions on Automation Science and Engineering 2025年 22卷 12118-12133页

作者： Lai, Jialun Wu, Zongze Ren, Zhigang Chen, Ci Tan, Qi Xie, Shengli Guangdong University of Technology School of Automation Guangdong Guangzhou510006 China Shenzhen University College of Mechatronics and Control Engineering Guangdong Shenzhen518052 China Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China

Trajectory planning with a learning-based approach has emerged as a crucial element in autonomous unmanned systems and has attracted substantial interest from both academia and industry. However, unresolved issues persist concerning data efficiency, safety, convergence, and generalization within the control pipeline. To address this gap, this work presents a trajectory planning method that combines the differential flatness of wheeled vehicle with global convergence property. Our proposed framework transforms the trajectory planning problem, integrating kinematic constraints into a motion planning paradigm. This transformation significantly reduces the state space associated with trajectory planning. Initially, Gaussian mixture regression (GMR) is employed to learn the nonlinear mapping from flat input, leveraging a limited number of demonstrations solved by the optimal control method. Subsequently, we design an asymmetric quadratic Lyapunov function that incorporates both random barrier information and the potential convergence property of the demonstration trajectories. Based on the optimized parameterized Lyapunov function incorporating the convergence and safety criteria, the analytical supplementary control is subsequently obtained by solving a quadratic programming problem to compensate for the prediction errors of GMR, which make the framework complete. Both numerical and real-world experiments are performed to validate the effectiveness of our framework. © 2004-2012 IEEE.

关键词： Motion planning

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Toward High-Accuracy and Low-Latency Group Vehicle Trajectory Prediction With Linear UNet-Enhanced Fully Connected Spatial–Temporal Graph Neural Network

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IEEE Internet of Things Journal 2025年第9期12卷 11797-11811页

作者： Zhang, Zirui Huang, Xumin Yu, Rong Wu, Maoqiang Kang, Jiawen Xie, Shengli Guangdong University of Technology School of Automation Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangdong Provincial Key Laboratory of Intelligent Systems and Optimization Integration Guangzhou510006 China South China Normal University School of Electronic Science and Engineering Foshan528225 China Guangdong University of Technology School of Automation 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangdong-Hong Kong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China

Group vehicle trajectory prediction (GVTP) is important for analyzing the traffic states and optimizing the traffic management. However, existing studies have performance bottlenecks in the prediction accuracy and inference latency. To tackle the problems, we propose a linear UNet-enhanced fully connected spatial–temporal GNN (LUFC-STGNN) for GVTP. First, a spatial graph is constructed by integrating prior-based and data-driven methods to capture both explicit and implicit spatial interactions between the vehicles. After that, a comprehensive temporal graph is created to capture the varying strengths of temporal interactions between all vehicles throughout historical timestamps. Furthermore, a fully connected spatial–temporal graph combining the spatial and temporal graphs is introduced to extract the effective spatial–temporal interaction features of the vehicles through the graph convolution operation. Finally, a linear UNet-based temporal dependency encoder (LU-TDE) is designed to further enhance the model’s ability of capturing the potential temporal patterns in the vehicle interactions. The encoder with linear complexity explores the multiscale temporal dependencies from the spatial–temporal interaction features but also reducing the inference latency. Experiments results based on real-world datasets show that compared to state-of-the-art models, our model reduces the average root mean square error over the 5-s prediction horizon by 31% and 10% on the NGSIM and HighD datasets, while reducing the inference latency by at least 1.26 times. © 2014 IEEE.

关键词： Prediction models

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Privacy-Enhanced Safe Reinforcement Learning for the Dispatch of a Local Energy Community

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IEEE Transactions on Smart Grid 2025年第3期16卷 2169-2183页

作者： Deng, Haoyuan Du, Ershun Wang, Yi The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong Hong Kong Tsinghua University Laboratory of Low Carbon Energy Beijing100084 China

Local Energy Community (LEC) has emerged as a viable community-focus framework to enhance local reliability and energy efficiency by integrating different energy sectors and managing local distributed energy resources (DERs). However, the difficulties associated with handling model complexity, along with privacy concerns arising from interactions between energy operators within the LEC, pose challenges for traditional algorithms in achieving coordinated dispatch. To this end, we develop a novel privacy-enhanced, safe, coordinated dispatch framework that integrates reinforcement learning (RL), the perturbation module, and the safety module. The private states of each energy sector within the LEC are concealed by the independent perturbation module before sharing. A central RL agent is then trained on the concealed state space to learn the optimal policy for coordinated dispatch under the complex and uncertain environment. Furthermore, dispatch actions are evaluated and refined by the safety module before the operators execute them. In this way, we can obtain an optimal policy without disclosing any sector’s private state while ensuring the safe operation of the LEC. Extensive experiments are carried out to validate the superior performance and scalability of the proposed method. © 2010-2012 IEEE.

关键词： Differential privacy

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Robust Output Regulation With Prescribed Performance for Second-Order Non-Polynomial Nonlinear Systems

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IEEE Transactions on Automation Science and Engineering 2025年 22卷 14234-14246页

作者： Liu, Wei Luo, Jialei Sun, Weijie South China University of Technology Key Laboratory of Autonomous Systems and Networked Control of Ministry of Education School of Automation Science and Engineering Guangzhou510641 China

This article focuses on the robust output regulation problem with prescribed performance for uncertain second-order nonlinear systems with non-polynomial nonlinearity. According to the existing framework for the robust output regulation problem of such nonlinear systems, we introduce some standard assumptions, and then construct two internal models of nonlinear forms to reproduce the steady-state information. Then, we convert the considered problem into the robust stabilization problem with prescribed performance of an augmented system containing the studied nonlinear system and the internal models. We incorporate a novel error transformation into the existing technique to design a stabilization controller, based on which we synthesize our control law to solve our original problem. Finally, we present two simulation examples to test our results. © 2004-2012 IEEE.

关键词： Nonlinear systems Performance evaluation Regulation Steady-state Transient analysis Polynomials Mathematical models Output feedback Adaptive systems Symmetric matrices

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1.27 GW/cm² Reverse Blocking E-Mode GaN-Si(100) Monolithic Heterogeneous Integration Cascode Switch With Ultralow Turn-On Voltage and Dynamic RON

IEEE Electron Device Letters

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IEEE Electron Device Letters 2025年第5期46卷 805-808页

作者： Fan, Yutong Zhang, Weihang Zhang, Yachao Wu, Yinhe Feng, Xin Liu, Zhihong Jiang, Yang In Mak, Pui Hao, Yue Zhang, Jincheng Xidian University National Key Laboratory of Wide Bandgap Semiconductor Devices and Integrated Technology School of Microelectronics Xi'an710071 China Guangzhou Institute of Technology Xidian University Guangzhou Wide Bandgap Semiconductor Innovation Center Guangzhou510555 China Institute of Microelectronics University of Macau State Key Laboratory of Analog and Mixed-Signal Vlsi China

In this letter, an E-Mode GaN-Si(100) monolithic heterogeneous integration cascode switch (RBMHIC-switch) with reverse blocking compatibility on a SiC substrate with an AlN buffer layer is demonstrated. The RBMHIC-switch with LGD of 22~μ m showed a threshold voltage (VTH) of 2.64 V, a large forward gate voltage swing of 16.27 V, a turn-on voltage (VON) of 0.3 V, and an extremely low reverse leakage current (IR) of 3.5× 10-4 mA/mm at -2000 V. The forward breakdown voltage (VFBR) of 2264 V and reverse breakdown voltage (VRBR) of 2183 V with a specific on-resistance (RON, SP ) of 3.74 mΩ ċ cm2 were achieved, resulting in a forward power figure of merit (PFOM = V BR2 /RON,SP) of 1.35 GW/cm2 and reverse PFOM of 1.27 GW/cm2. The ratio between dynamic-RON and static-RON was 1.104 and 1.127 after a 10 ms 1900 V drain stress voltage (V DS- OFF ) and a 10 ms -1900 V V DS- OFF (delay time = 1~μ s) for the RBMHIC-switches, respectively, which were the best among all existing GaN power devices with reverse blocking capability. The thermal resistance at different temperatures was also extracted. The RBMHIC-switche shows decent overall characteristics as compared to the reported results. © 1980-2012 IEEE.

关键词： Gallium nitride

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SnS/WSe van der Waals Single-Detector Spectrometer With a Dynamically Selecting Spectral Reconstruction Strategy

IEEE Electron Device Letters

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IEEE Electron Device Letters 2025年第5期46卷 801-804页

作者： Zhou, Yang Mu, Haoran Zhang, Congwen Xu, Renjing Zhang, Guangyu Lin, Shenghuang Songshan Lake Materials Laboratory Dongguan523808 China South China Normal University Moe Key Laboratory of Laser Life Science Guangdong Provincial Key Laboratory of Laser Life Science Guangzhou Key Laboratory of Spectral Analysis and Functional Probes College of Biophotonics Guangzhou510631 China The Hong Kong University of Science and Technology Microelectronics Thrust Guangzhou511442 China Institute of Physics Chinese Academy of Science Beijing100190 China

The single-detector spectrometers based on 2D layer van der Waals (vdW) heterojunctions offer advantages in spectral reconstruction due to their high sensitivity, tunable optical properties, and the ability to cover a broad spectral range. There exist two principal algorithms dominating spectrum reconstruction for this kind spectrometer: the Tikhonov regularization method combined with the Least Squares Method (LSM) and neural network-based approaches, particularly Deep Learning (DL). However, both of the algorithms exhibit inherent limitations in spectral reconstruction, which constrain the versatility of computational spectrometers that rely solely on a single algorithm for reconstructing diverse spectral profiles. To overcome this limitation, we introduce an artificial neural network (ANN)-based classification model capable of dynamically selecting the optimal algorithm throughout the reconstruction process. This enables highly accurate spectral reconstruction within the 440-700 nm wavelength range, achieving a spectral resolution of 6 nm. By harnessing the complementary strengths of multiple algorithms, our approach proposes a novel strategy for combining techniques to enhance the precision of spectral reconstructions, laying the groundwork for more sophisticated methods in the future. © 1980-2012 IEEE.

关键词： Van der Waals forces

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Subband Processing for OQAM Digital-Subcarrier-Multiplexing Optical Coherent Systems

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Journal of Lightwave Technology 2025年第9期43卷 4218-4232页

作者： Fan, Zhaoquan Luo, Jiating Zheng, Bofang Cai, Yi Zhao, Jian South China University of Technology School of Electronic and Information Engineering Guangzhou510640 China Huawei Technologies Company Ltd. Shenzhen518129 China Soochow University School of Electronic and Information Engineering Suzhou215006 China

High baud-rate signal with wide spectral width not only requires high-complexity digital signal processing (DSP) in coherent receiver, but also is susceptible to transmission impairments including optical filtering in the link. In this paper, we propose a novel subband-based DSP structure, which decomposes the signal into narrower-bandwidth subbands, performs chromatic dispersion compensation (CDC) and butterfly adaptive filtering (BAF) on a subband basis, and then synthesizes the processed subbands. In this way, the proposed structure achieves an arbitrary spectral processing resolution regardless of the transmitted signal spectral profile and reduces the required size of fast Fourier transform for CDC and the tap number for BAF. We investigate the proposed subband structure in offset-quadrature-amplitude-modulation digital subcarrier multiplexing systems. Simulations at 140 GBaud and experiments at 40 GBaud show that this scheme achieves a faster convergence rate, and enhanced tolerance to optical filtering, rotation of state of polarization and delay of the BAF feedback loop. It also reduces the complexity of CDC and BAF to 30% of that using conventional DSP structure, even taking subband decomposition and synthesis into account. © 1983-2012 IEEE.

关键词： Adaptive filtering

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Knowledge Efficient Federated Continual Learning for Industrial Edge Systems

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IEEE Transactions on Network Science and Engineering 2025年第3期12卷 2107-2120页

作者： Chen, Jiao He, Jiayi Tang, Jianhua Li, Weihua Yin, Zihang South China University of Technology Shien-Ming Wu School of Intelligent Engineering Guangzhou511442 China Pazhou Lab Guangzhou510335 China South China University of Technology School of Mechanical and Automotive Engineering Guangzhou510641 China China Academy of Information and Communications Technology Beijing100083 China

Recent advances in federated learning (FL) primarily focus on addressing inter-client data heterogeneity, implicitly assuming static data within each client. However, this assumption is inadequate for industrial edge systems (IES), which operate in dynamically changing environments and require real-time processing and analysis of voluminous time-series data generated by the Internet of Things. To bridge this gap, we propose MeCo, a novel federated continual learning (FCL) method for IES, designed to avoid forgetting past knowledge while continuously adapting to new task data. MeCo distinguishes itself from traditional FL by effectively addressing both inter-client and intra-client data heterogeneity through a knowledge-efficient strategy. Specifically, it includes: Meta task-invariant knowledge consolidation, which helps capture shared features across tasks to alleviate forgetting;Consistent task-specific knowledge transfer, which allows edge clients to extract relevant knowledge from a server-side knowledge pool, providing a jump-starting for the current task. Experimental results demonstrate that MeCo significantly outperforms other federated and/or continual learning approaches in real-world industrial fault diagnosis, achieving approximately 2% higher Mean Average Accuracy and being 1.74 times more cost-effective in server-to-client communication. These advantages, along with its robust performance in IES, indicate the potential of MeCo for facilitating edge-cloud collaborative learning in the future. © 2013 IEEE.

关键词： Federated learning

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Revisiting Location Privacy in MEC-Enabled Computation Offloading

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IEEE Transactions on Information Forensics and Security 2025年 20卷 4396-4407页

作者： Li, Jingyi Ou, Wenzhong Ouyang, Bei Ye, Shengyuan Zeng, Liekang Chen, Lin Chen, Xu Sun Yat-sen University School of Computer Science and Engineering Guangzhou510275 China Chinese University of Hong Kong Department of Information Engineering Hong Kong Macao Polytechnic University Engineering Research Centre of Applied Technology on Machine Translation and Artificial Intelligence China

Mobile Edge Computing (MEC) revolutionizes real-time applications by extending cloud capabilities to network edges, enabling efficient computation offloading from mobile devices. In recent years, the location privacy concern within MEC offloading has been recognized, prompting the proposal of various methodologies to mitigate this concern. However, this paper demonstrates that the prevailing privacy protection methods exhibit vulnerabilities. First, we analyze the shortcomings of current methodologies through both system modeling and evaluation metrics. Then, we introduce a Learning-based Trajectory Reconstruction Attack (LTRA) to expose the weaknesses, achieving up to 91.2% reconstruction accuracy against the state-of-the-art protection method. Further, based on w-event differential privacy, we propose an l-trajectory differentially private mechanism, i.e., OffloadingBD. Compared to the existing works, OffloadingBD provides more flexible and enhanced protection with sound privacy theoretical guarantee. Lastly, we conduct extensive experiments to evaluate LTRA and OffloadingBD. The experiment results show that LTRA has good generalization ability and OffloadingBD showcases a superior balance between privacy and utility compared with baselines. © 2005-2012 IEEE.

关键词： Differential privacy

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