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Distributed Leaderless Optimal Output Consensus of Heterogeneous Multi-Agent Systems

Journal of Systems Science & Complexity 2025年第2期38卷 671-690页

作者： ZHANG Liping ZHANG Huanshui XIE Lihua College of Electrical Engineering and Automation Shandong University of Science and Technology School of Control Science and Engineering Shandong University School of Electrical and Electronic Engineering Nanyang Technological University

The distributed optimal output synchronization problem for the leaderless heterogeneous multi-agent system with a general global cost function is investigated for the first time by linear quadratic(LQ) optimal control theory. Conventional algorithms for heterogeneous systems are quite complex, requiring the design of a virtual reference generator and the solving of regulation *** paper presents a novel distributed asymptotically optimal controller by incorporating the design of distributed observer and feedforward controller. A general form of the distributed controller is obtained by solving an augmented algebraic Riccati equation, which is parallel to classical optimal control theory. The optimal topology is an arbitrary directed graph containing only a spanning tree. It is shown that the proposed algorithms outperform the traditional consensus methods in the convergence speed by selecting proper observer gain matrices, and eliminate the reliance on the nonzero eigenvalues of Laplacian matrix. Simulation example further demonstrates the effectiveness of the proposed scheme and a faster superlinear convergence speed than the existing algorithm.

关键词： Distributed feedforward control heterogeneous systems leaderless output consensus optimal control

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High-Sensitivity and External RI-Independent Mode-Selective Michelson Interferometer Using a Single-Ended Helical Graded-Index Fiber

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Journal of Lightwave Technology 2025年第11期43卷 5336-5344页

作者： Zhu, Chengliang Meng, Xinyue Shi, Mengshuo Cheng, Tonglei Zhao, Yong Northeastern University College of Information Science and Engineering Shenyang110004 China Northeastern University School of Control Engineering Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao066004 China State Key Laboratory of Synthetical Automation for Process Industries Shenyang China Northeastern University School of Control Engineering Qinhuangdao066004 China

We have theoretically and experimentally proposed and demonstrated an all-in-fiber-core mode-selective Michelson interferometer (MS-MI) sensor utilizing a single-ended helical graded-index few-mode fiber (SE-HGIFMF). The SE-HGIFMF effectively eliminates multi-mode interference, remains insensitive to external refractive index (RI) variations, and significantly enhances sensing sensitivity due to the minimal disparity in group RI between the two guided core modes. We investigated the torsion, strain, and temperature characteristics of the sensor, achieving sensitivities of 1.785 nm/(rad/m) for directional torsion, 0.0316 nm/μϵ for strain, and 0.624 nm/°C for temperature. These sensitivities are competitive within the fiber optic sensor family and represent the highest values reported for mode-selective MI and Mach-Zehnder interferometer (MZI) sensors to our knowledge. The proposed sensor not only demonstrates exceptional sensitivity but also offers directionality identification for torsion, ease of use, flexible installation, low loss, and high reliability, showcasing robustness against environmental fluctuations. © 1983-2012 IEEE.

关键词： Michelson interferometers

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Coprime Factorization-Based Encryption and Attack Detection for Nonlinear Cyber-Physical Systems Using Deep Learning Approach

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IEEE Transactions on automation Science and engineering 2025年 22卷 14020-14029页

作者： Wu, Shimeng Luo, Hao Zhang, Jiusi Qiao, Xinyu Tian, Jilun Jiang, Yuchen Harbin Institute of Technology Department of Control and Simulation Center Harbin150001 China National Key Laboratory ofComplex System Control and Intelligent Agent Cooperation Harbin150001 China University of Electronic Science and Technology of China School of Automation Engineering Chengdu611731 China

This paper presents a data-driven framework for integrating encryption transmission and attack detection in cyber-physical systems (CPS) with nonlinear physical plants. The main focus of this research is to use deep neural networks to realize the coprime factorization (CF) of nonlinear systems. The definition of the CF guides the network training and designing process, and the model’s topology is designed in the state-space form, which improves the interpretability of the data-driven CF. Based on the CF-aided neural networks, an encrypted transmission module is designed that projects information related to system dynamics into a perpendicular data space, which complements existing encryption methods from a control theory perspective. Subsequently, an anomaly detector are designed using the same CF pairs. This detector not only provides high-accuracy detection of attacks but also distinguishes between attacks and faults, thereby reducing the false positive rate and enhancing the reliability of the attack detection. The proposed method has been validated in a real CPS using a mecanum-wheeled vehicle as the physical plant, demonstrating its effectiveness and applicability. © 2004-2012 IEEE.

关键词： Factorization

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Dynamic Modeling and control for an Offshore Semisubmersible Floating Wind Turbine

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IEEE Transactions on automation Science and engineering 2025年 22卷 12371-12382页

作者： Gong, Yingjie Yang, Qinmin Geng, Hua Meng, Wenchao Wang, Lin Zhejiang University State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou310027 China Huzhou Institute Zhejiang University State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou310027 China Tsinghua University Beijing National Research Center for Information Science and Technology Department of Automation Beijing100084 China Zhejiang Windey Company Ltd. Hangzhou310027 China

Floating wind turbines (FWTs) hold significant potential for the exploitation of offshore renewable energy resources. Nevertheless, prior to the construction of FWTs, it is imperative to tackle several critical challenges, especially the issue of performance degradation under combined wind and wave loads. This study initiates with the development of a simplified nonlinear dynamical model for a semi-submersible FWT. In particular, both the rotor dynamics and the finite rotations of the platform are considered in presented modeling approach, thereby effectively capturing the complex interplay between the platform, tower, nacelle, and rotor under combined wind and wave loads. Subsequently, based on the developed FWT model, a novel adaptive nonlinear pitch controller is formulated with the goal of striking a trade-off between regulating power generation and reducing platform motion. Notably, the proposed control strategy adopts a continuous control approach, strategically beneficial in circumventing the chattering phenomenon commonly associated with sliding mode control. Furthermore, the controller integrates an online approximator and a robust integral of the sign of the tracking error, facilitating real-time learning of system unknown dynamics while compensating for bounded disturbances. Finally, both the accuracy of the established nonlinear FWT model in predicting key dynamics and the superiority of the presented pitch controller are validated through comprehensive comparative studies. Note to Practitioners - This paper addresses the conflicting goals between power regulation and load mitigation for floating wind turbines (FWTs) to ensure the reliable operation of wind turbine systems. This remains an ongoing challenge due to the inherent complexity of existing FWT models, frequently resulting in controllers crafted using linearized representations that fail to accommodate real-world uncertainties effectively. Through the utilization of a simplified physical-based n

关键词： Renewable energy

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Resilient time-varying formation optimal tracking for heterogeneous multi-agent systems with coupled constraints

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IEEE Transactions on automation Science and engineering 2025年 22卷 15274-15285页

作者： Su, Lingfei Lin, Zhi Hua, Yongzhao Shi, Zhexin Dong, Xiwang Lu, Jinhu Wang, Danwei Beihang University School of Artificial Intelligence Institute of Artificial Intelligence Beijing100191 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Beihang University School of Automation Science and Electrical Engineering Shenyuan Honors College Beijing100191 China Beijing100191 China Science and Technology on Aircraft Control Laboratory Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Zhongguancun Laboratory Beijing100094 China Nanyang Technological University School of Electrical and Electronic Engineering 639798 Singapore

Formation constrained optimal tracking problems for heterogeneous multi-agent systems under Byzantine attacks are studied. The objective of the honest agents, unaffected by Byzantine attacks, is to find optimal trajectories that minimize the cumulative local cost functions of all honest agents, while simultaneously satisfying the cumulative local inequality constraints and maintaining the formation configuration. A distributed resilient formation tracking controller utilizing preview control and primal-dual strategy is proposed without requiring each agent to know which agents are affected by Byzantine attacks. The performance of the proposed algorithm is analyzed in terms of the upper bounds of dynamic regret and constraint violations. Numerical simulations and experiments, involving one unmanned aerial vehicle and four unmanned ground vehicles, are performed to verify the effectiveness of the obtained results. © 2004-2012 IEEE.

关键词： Cost functions

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Industrial Gearbox Fault Diagnosis Based on Vision Transformer and Infrared Thermal Imaging

Industrial Gearbox Fault Diagnosis Based on Vision Transform...

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TEPEN International Workshop on Fault Diagnostics and Prognostics, TEPEN-IWFDP 2024

作者： Li, Yan Cao, Xunqi Wang, Haoyu Yu, Kun Zhang, Yongchao School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China School of Mechanical Engineering and Automation Northeastern University Shenyang110819 China

ISBN: (纸本)9783031734069

Infrared thermal imaging technology measures the intensity of external radiation emitted by objects, producing thermal images that depict their outlines and surface temperature distribution. This method holds tremendous potential for monitoring the health status of gearboxes, offering a highly promising approach to ensure their optimal performance and longevity. By utilizing the rich features of thermal images, it is possible to distinguish the health status of gearboxes from a new technological perspective. However, the traditional infrared thermal imaging technology method based on Convolutional Neural Network (CNN) has limitations in capturing the overall features and contextual relationships of images. Vision Transformer (ViT) network uses self-attention mechanism to fully explore the correlation within features, which can effectively represent the global information of high-dimensional features. Therefore, this article proposes a ViT network method for gearbox fault diagnosis by using infrared thermal imaging. The effectiveness of this method is verified by using experimental data from an industrial gearbox. Compared with other comparison methods, the proposed method exhibits better diagnostic results. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Federated Accelerated Deep Reinforcement Learning for Multi-Zone HVAC control in Commercial Buildings

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IEEE Transactions on Smart Grid 2025年第3期16卷 2599-2610页

作者： Xia, Yihan Wang, Xinli Yin, Xiaohong Bo, Wanlin Wang, Lei Li, Shaoyuan Li, Kang Shandong University School of Control Science and Engineering Jinan250100 China Qingdao University of Science and Technology College of Automation and Electronic Engineering Qingdao266101 China University of Leeds School of Electronic and Electric Engineering LeedsLS2 9JT United Kingdom

Deep Reinforcement Learning (DRL) has demonstrated a promising prospect in optimizing Heating, Ventilation, and Air Conditioning (HVAC) systems to minimize energy costs and improve thermal comfort without requiring the knowledge of building thermal dynamic models. However, DRL training can be difficult to converge due to its long exploration, especially in multi-agent scenarios. To address these concerns, we propose a novel Federated Accelerated Multi-Agent DRL (FA-MADRL) algorithm for HVAC control in commercial buildings with multi-zone offices. To be specific, we reformulate the optimal control problem of indoor temperature, CO2 concentration and humidity in multi-zone HVAC as a Markov Decision Process (MDP). Then, we establish a MADRL framework for HVAC system control and utilize a federated learning (FL) mechanism to accelerate the convergence during real-time deployment. Experimental studies have been carried out on a TRNSYS-based commercial building HVAC system with multiple zones. The results demonstrate the superiority of our proposed algorithm, with improved convergence speed, reduced energy consumption, and satisfied thermal comfort. © 2010-2012 IEEE.

关键词： Deep reinforcement learning

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Optimal Scheduling of an Independent Electro-Hydrogen System with Hybrid Energy Storage Using a Multi-Objective Standardization Fusion Method

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Energy engineering 2025年第1期122卷 63-84页

作者： Suliang Ma Zeqing Meng Mingxuan Chen Yuan Jiang School of Electrical and Control Engineering North China University of TechnologyBeijing100144China ChinaThree Gorges Technology Co. Ltd.Beijing100032China School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing100083China

In the independent electro-hydrogen system(IEHS)with hybrid energy storage(HESS),achieving optimal scheduling is ***,it presents a challenge due to the significant deviations in values ofmultiple optimization objective functions caused by their physical *** deviations seriously affect the scheduling process.A novel standardization fusion method has been established to address this issue by analyzing the variation process of each objective function’s *** optimal scheduling results of IEHS with HESS indicate that the economy and overall energy loss can be improved 2–3 times under different optimization *** proposed method better balances all optimization objective functions and reduces the impact of their *** the cost of BESS decreases by approximately 30%,its participation deepens by about 1 ***,if the price of the electrolyzer is less than 15¥/kWh or if the cost of the fuel cell drops below 4¥/kWh,their participation will increase *** study aims to provide a more reasonable approach to solving multi-objective optimization problems.

关键词： Electro-hydrogen system multi-objective optimization standardization method hybrid energy storage system

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An algorithm for indoor positioning based on LightGBM + ExtraTrees chaotic weighted ensemble: evaluation and comparison

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Journal of Supercomputing 2025年第7期81卷 1-31页

作者： Zhang, Lieping Chen, Yao Gao, Xiaolin Zheng, Xinpeng Zhang, Cui Guangxi Key Laboratory of Special Engineering Equipment and Control Guilin University of Aerospace Technology Guilin China Key Laboratory of Advanced Manufacturing and Automation Technology Guilin University of Technology Guilin China School of Information Engineering Nanning College of Technology Guilin China

To address the issues of insufficient positioning accuracy and poor stability in existing indoor positioning algorithms, this paper proposes an indoor fingerprint positioning algorithm based on LightGBM and ExtraTrees chaotic weighted ensemble. First, the received signal strength indicator (RSSI) signal strengths of wireless fidelity (WiFi) at different locations are collected using a mobile device to construct a fingerprint database. Kalman filtering is then applied to preprocess the fingerprint data, removing outliers and noise to improve the data quality. The preprocessed dataset is subsequently divided into training and testing sets. Light gradient boosting machine (LightGBM) and extremely randomized trees (ExtraTrees) are used for modeling, and the chaos particle swarm optimization (CPSO) algorithm is employed to optimize the key parameters of both LightGBM and ExtraTrees. The optimal parameter configuration is determined based on comprehensive evaluation metrics. Finally, the optimal weight ratio of LightGBM and ExtraTrees models is determined through the CPSO algorithm for weighted fusion. Experimental results demonstrate that the proposed algorithm achieves an average positioning error of 1.1 m, which represents a reduction of 7.5–26.7% in average positioning error compared to LightGBM, ExtraTrees, and the LightGBM + ExtraTrees algorithms. After introducing random noise, the proposed algorithm exhibits the smallest variation in average positioning error, validating its significant advantages in positioning accuracy and anti-interference ability. However, the algorithm in this study is primarily based on static WiFi fingerprint data and has not yet been validated in dynamic environments. Future research should further explore its applicability and robustness in more complex environments. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Particle swarm optimization (PSO)

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Research on LSTM-based winding state prediction method 7

Research on LSTM-based winding state prediction method

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2024 7th International Conference on Power Electronics and control engineering, ICPECE 2024

作者： Wang, Yiyang Guo, Haifeng Li, Jianan Liu, Xinen Li, Xiang Du, Zhongbo Shen, Yang School of Electrical and Automation Engineering Liaoning Institute of Science and Technology Benxi117004 China Liaoning Province Robot Drive Control Engineering Laboratory Benxi117004 China

Mechanical and electrical equipment is widely used in various links of the manufacturing industry and is also the key to the implementation of modern industrial technology. Winding is the core component of transformers, electromagnetic valves, motors, robots, and other mechanical and electrical equipment. The safe and reliable operation of winding is the premise for the healthy operation of industrial equipment. Therefore, taking the core component of many industrial equipment, the winding, as the research object, and aiming to improve the safety and reliability of the equipment, this study focuses on the state prediction method of winding with the help of sensitive feature information. With the rapid development of industrial big data technology, data-driven technology, especially time series prediction technology, has developed rapidly. Considering the characteristics of winding degradation data, a winding state prediction method based on the Long Short-Term Memory (LSTM) approach is proposed. By using experimental data of winding, a health index reflecting their health status is constructed, and the LSTM is utilized for training the health index, thereby achieving time series-based state prediction of winding. This provides reliable support for the health status assessment of industrial mechanical and electrical equipment and enhances the reliability of the equipment. © Published under licence by IOP Publishing Ltd.

关键词： Machine windings

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