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Consensus of Continuous-Time Linear Multiagent Systems With Discrete Measurements

IEEE TRANSACTIONS ON CYBERNETICS 2022年第5期52卷 3196-3206页

作者： Wang, Xiaoling Jiang, Guo-Ping Su, Housheng Zeng, Zhigang Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Coll Artificial Intelligence Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Jiangsu Engn Lab IoT Intelligent Robots Nanjing 210023 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Educ Minist China Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

This article concerns the robust consensus problem of continuous-time linear multiagent systems (MASs) with uncertainty and discrete-time measurement information, where the output measurement information is in the data-sampled form. Distributed output-feedback protocol with or without controller interaction is proposed for each agent. Specifically, the output-feedback protocol runs in continuous time with an output error correction term mixed with the discrete-time measurement information. The concrete algorithm is given for the construction of the feedback matrices. Then, by using the delay-input approach, sufficient conditions are provided for the robust consensus of this kind of MASs interacting over networks described by the directed graphs. Finally, numerical simulations are given to illustrate the theoretical results.

关键词： Protocols Observers Robustness Symmetric matrices Uncertainty Multi-agent systems Measurement uncertainty Consensus continuous-time MAS discrete-time measurement output feedback

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A reinforcement learning-based dynamic multi-objective optimization approach for integrated timetabling and vehicle scheduling

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Knowledge-Based Systems 2025年 321卷

作者： Shen, Yindong Xie, Wenliang Institute of Artificial Intelligence Huazhong University of Science and Technology Wuhan430074 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The MOE Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan430074 China

Dynamic integrated timetabling and vehicle scheduling (D-ITVS) is essential for mitigating the negative impacts of service disruptions. It involves multiple rescheduling stages, with inherent optimization similarities across these stages. However, existing optimization approaches for the D-ITVS problem have not systematically exploited these similarities, overlooking the potential for decision knowledge from previous stages to inform the current stage. To address this gap, this paper proposes a reinforcement learning-based dynamic multi-objective optimization approach (RL-DMOA), which focuses on transferring decision knowledge between rescheduling stages. This approach models the optimization process of each rescheduling stage in the D-ITVS problem as a Markov decision process, incorporating a state space with vehicle information, action space for vehicle assignment, and a multi-objective reward function. A multi-objective deep reinforcement learning (M-DRL) agent is employed within the RL-DMOA to select actions based on the state at each decision point. The agent is constructed on a multi-objective deep Q-learning network (M-DQN), with a Q-value adjustment layer incorporated to prevent the selection of invalid actions. To select optimal actions while balancing the conflicts among multiple objectives, the M-DRL agent applies a non-dominated sorting selection strategy. Experimental results demonstrate that the proposed RL-DMOA is capable of generating timetables and vehicle schedules with reduced costs, enhanced robustness, and improved convergence and diversity across all rescheduling stages. By balancing operational costs and passenger service quality, these improvements benefit transit operators, and during daily operations, passengers enjoy reduced travel costs and enhanced service reliability. © 2025 Elsevier B.V.

关键词： Reinforcement learning

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A multi-perspective framework for seismic retrofit optimization of urban infrastructure systems

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EARTHQUAKE ENGINEERING & STRUCTURAL DYNAMICS 2022年第11期51卷 2771-2790页

作者： Liu, Chuang Ouyang, Min Mao, Zijun Xu, Xiaolin Huazhong Univ Sci & Technol Coll Publ Adm Wuhan Peoples R China Huazhong Univ Sci & Technol Innovat Inst Wuhan Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan Peoples R China Huazhong Univ Sci & Technol Key Lab Image Informat Proc & Intelligent Control Minist Educ Wuhan Peoples R China

Urban infrastructure systems play essential roles in the smooth functioning of modern society but are also threatened by seismic hazards in the earthquake-prone areas. Retrofitting critical components of those systems has been considered as the most frequently used mitigation strategy in both the literature and practice. The seismic retrofit budget is usually limited, then it needs to identify a set of critical components to be retrofitted, which is generally formulated as a seismic retrofit optimization problem. This article proposes a multi-perspective modeling and solution framework for the seismic retrofit optimization of urban infrastructure systems, which allows choosing different performance measures including vulnerability, resilience loss and economic loss as the objective function. The proposed framework can be used to explore how different performance measures and the infrastructure interdependencies affect the seismic retrofit decision. Taking the interdependent Shelby power and gas systems as an example, results show that if considering single systems, the optimal economic loss-based performance improvement ratio (PIR) is larger than the best resilience loss-based PIR, which is larger than the vulnerability-based PIR;if considering interdependent systems, the interdependency intensity is indeed a key factor affecting the retrofit decision.

关键词： multi-perspective framework resilience seismic retrofit urban infrastructure systems

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Moving Target Surrounding control of Linear Multiagent Systems With Input Saturation

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2022年第3期52卷 1705-1715页

作者： Xu, Bowen Zhang, Hai-Tao Meng, Haofei Hu, Binbin Chen, Duxin Chen, Guanrong Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China Southeast Univ Sch Math Nanjing 211189 Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China

Formation control finds broad applications in numerous fields, such as cooperative detection, surveillance, transportation, and disaster rescue. In this article, aiming at hunting a moving target, a two-stage surrounding control algorithm is proposed for linear multiagent systems subject to input saturations. An adaptive distributed observer is developed for each agent to reconstruct the target's position. With the assistance of the algebraic graph theory and low gain feedback technique, distributed controllers are designed to drive the agents to encircle the moving target with a fixed radius and evenly distributed phase angles. Finally, both numerical simulations and experiments are conducted to verify the effectiveness of the proposed control algorithm.

关键词： Task analysis Multi-agent systems Sensors Mobile robots Synchronization Protocols Observers Distributed estimation input saturation multiagent systems (MASs) surrounding control

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Fixed-Time Synchronization of Competitive Neural Networks With Multiple Time Scales

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第8期33卷 4133-4138页

作者： Yang, Wu Wang, Yan-Wu Morarescu, Irinel-Constantin Liu, Xiao-Kang Huang, Yuehua Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Guangxi Univ Sch Elect Engn Nanning 530004 Peoples R China Univ Lorraine CNRS CRAN F-54000 Nancy France China Three Gorges Univ Coll Elect Engn & New Energy Yichang 443002 Peoples R China

In this brief, we investigate the fixed-time synchronization of competitive neural networks with multiple time scales. These neural networks play an important role in visual processing, pattern recognition, neural computing, and so on. Our main contribution is the design of a novel synchronizing controller, which does not depend on the ratio between the fast and slow time scales. This feature makes the controller easy to implement since it is designed through well-posed algebraic conditions (i.e., even when the ratio between the time scales goes to 0, the controller gain is well defined and does not go to infinity). Last but not least, the closed-loop dynamics is characterized by a high convergence speed with a settling time which is upper bounded, and the bound is independent of the initial conditions. A numerical simulation illustrates our results and emphasizes their effectiveness.

关键词： Synchronization Neural networks Neurons Process control Visualization Perturbation methods Pattern recognition Competitive neural network continuous control method fixed-time synchronization multiple time-scale feature

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Resilience-driven repair sequencing decision under uncertainty for critical infrastructure systems

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2022年第0期221卷 108378-108378页

作者： Xu, Min Ouyang, Min Hong, Liu Mao, Zijun Xu, Xiaolin Huazhong Univ Sci & Technol Coll Publ Adm Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Innovat Inst Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat China 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

Disruptions on critical infrastructure systems (CISs) might affect their normal operation and cause severe social and economic impact. The rapid recovery of post-disaster CISs is crucially important. Based on the authors' previous work on the deterministic formulation of the repair sequencing decision problem of post-disaster CISs under limited repair resources, this paper considers the uncertainty of the repair time of damaged components and proposes a two-stage stochastic model. To solve the stochastic model, a scenario generation and reduction method is first used to generate a limited number of repair time scenarios, and then an efficient enumeration algorithm is proposed for small-scale disruptions and adopted as a module in a heuristic algorithm for large-scale disruptions. Numerical experiments on three systems are conducted to demonstrate the efficiency of the proposed algorithm. Results show that the proposed algorithm can be applied to the recovery decision of large-scale CISs with extensive disruptions. In addition, results show that for most instances, the stochastic solution solved using a limited number of repair time scenarios might be worse than the solution solved from the deterministic model when judging the two solutions by a huge number of repair time scenarios to sufficiently capture the repair time uncertainty.

关键词： Critical infrastructure systems Resilience Repair sequencing decision Stochastic model Uncertainty

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Reduced-order interval observer based consensus for MASs with time-varying interval uncertainties

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AUTOMATICA 2022年 135卷 109989-109989页

作者： Wang, Xiaoling Wang, Xiaofan Su, Housheng Lam, James Nanjing Univ Posts & Telecommun Coll Automat & AI Nanjing 210023 Peoples R China Jiangsu Engn Lab IOT Intelligent Robots IOTRobot Nanjing 210023 Peoples R China Shanghai Univ Dept Automat Shanghai 200072 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Image Proc & Intelligent Control Key Lab Educ Minist China Luoyu Rd 1037 Wuhan 430074 Peoples R China Univ Hong Kong Dept Mech Engn Pokfulam Rd Hong Kong Peoples R China

In this paper, the consensus problem of a continuous-time uncertain multi-agent system (MAS) with time-varying interval uncertainties in the system matrix and unknown initial states is investigated via output feedback. Relating only to the interval bounds on the uncertainties, a reduced-order framer, which includes two Luenberger-like observers, is first derived. Then, a distributed control algorithm relying only on the information of the framers associated with each agent and its neighbors is proposed. It is shown that the control algorithm can render the reduced-order framer to be an exponential reduced-order interval observer, and simultaneously drive the uncertain MAS to reach robust consensus exponentially. Finally, numerical simulations for a large-scale MAS are used to verify the theoretical results. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Consensus Interval observer Multi-agent system Reduced-order observer Uncertain system

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SIAVC: Semi-Supervised Framework for Industrial Accident Video Classification

arXiv

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arXiv 2024年

作者： Li, Zuoyong Lin, Qinghua Fan, Haoyi Zhao, Tiesong Zhang, David The Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China The School of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China The School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou45000 China The Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information Fuzhou University Fuzhou350108 China Shenzhen518172 China

Semi-supervised learning suffers from the imbalance of labeled and unlabeled training data in the video surveillance scenario. In this paper, we propose a new semi-supervised learning method called SIAVC for industrial accident video classification. Specifically, we design a video augmentation module called the Super Augmentation Block (SAB). SAB adds Gaussian noise and randomly masks video frames according to historical loss on the unlabeled data for model optimization. Then, we propose a Video Cross-set Augmentation Module (VCAM) to generate diverse pseudo-label samples from the high-confidence unlabeled samples, which alleviates the mismatch of sampling experience and provides high-quality training data. Additionally, we construct a new industrial accident surveillance video dataset with frame-level annotation, namely ECA9, to evaluate our proposed method. Compared with the state-of-the-art semi-supervised learning based methods, SIAVC demonstrates outstanding video classification performance, achieving 88.76% and 89.13% accuracy on ECA9 and Fire Detection datasets, respectively. The source code and the constructed dataset ECA9 will be released in https://***/AlchemyEmperor/SIAVC. Copyright © 2024, The Authors. All rights reserved.

关键词： Classification (of information)

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Prescribed convergence analysis of recurrent neural networks with parameter variations

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MATHEMATICS AND COMPUTERS IN SIMULATION 2021年 182卷 858-870页

作者： Bao, Gang Zeng, Zhigang China Three Gorges Univ Hubei Key Lab Cascaded Hydropower Stat Operat & C Yichang 443002 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Educ Minist China Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

Recurrent neural networks are designed to be convergent to the desired equilibrium point for their applications. Network parameter variations lead network states to other different points. So this paper discusses the prescribed convergence problem of recurrent neural networks with parameter variations. Firstly, we recurrent neural networks' equilibrium point variation principles when parameters are changed. Then we design one track controller to make recurrent neural networks be convergent to the prescribed equilibrium for known parameter variations. Next, we present one adaptive controller to lead network states to the desired equilibrium for unknown parameter variations. At last, two examples are given for validating the presented methods. (C) 2020 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

关键词： Convergence Stability Recurrent neural network Equilibrium point

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NPGA: A Unified Algorithmic Framework for Decentralized Constraint-Coupled Optimization

arXiv

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arXiv 2022年

作者： Li, Jingwang Su, Housheng School of Artificial Intelligence and Automation The Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Huazhong University of Science and Technology Wuhan430074 China

This work focuses on a class of general decentralized constraint-coupled optimization problems. We propose a novel nested primal-dual gradient algorithm (NPGA), which can achieve linear convergence under the weakest known condition, and its theoretical convergence rate surpasses all known results. More importantly, NPGA serves not only as an algorithm but also as a unified algorithmic framework, encompassing various existing algorithms as special cases. By designing different network matrices, we can derive numerous versions of NPGA and analyze their convergences by leveraging the convergence results of NPGA conveniently, thereby enabling the design of more efficient algorithms. Finally, we conduct numerical experiments to compare the convergence rates of NPGA and existing algorithms, providing empirical evidence for the superior performance of NPGA. Copyright © 2022, The Authors. All rights reserved.

关键词： Consensus algorithm

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