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Distributed Output-Feedback consensus Control of Multiagent Systems with Unknown Output Measurement Sensitivity

IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2021年第7期66卷 3303-3310页

作者： Li, Kuo Hua, Chang-Chun You, Xiu Guan, Xin-Ping Yanshan Univ Inst Elect Engn Qinhuangdao 066004 Hebei Peoples R China Shanxi Univ Sch Math Sci Taiyuan 030006 Peoples R China Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China

This article addresses the leader-following consensus problem for multiagent systems with unknown output measurement sensitivity based on a fixed directed topology. Different from the existing works on the consensus problem, the output measurement sensitivity is described as a total unknown parameter. Under this nonideal condition, we put forward a distributed output-feedback consensus control algorithm for the first time. To start with, we design the novel distributed high-gain observer and a high-gain K-filter for each follower, which are utilized to reconstruct the state of the leader and the follower, respectively. Subsequently, the consensus problem is transformed into the stability problem by introducing appropriate state transformation. By using the backstepping control method, the distributed output-feedback controller with an adaptive law is designed for each follower with the help of a Nussbaum-type function. Based on the Lyapunov stability theory, it is strictly proved that all agents can achieve a consensus with the designed controller. Finally, the numerical simulation is presented to illustrate the effectiveness of theoretical results.

关键词： Topology Output feedback Multi-agent systems Sensitivity Observers consensus algorithm Heuristic algorithms consensus control distributed observer K-filter multiagent systems unknown output measurement sensitivity

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Resilient consensus with Multi-Hop Communication

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IEEE Transactions on Automatic Control 2025年

作者： Yuan, Liwei Ishii, Hideaki Hunan University College of Electrical and Information Engineering Changsha410082 China The University of Tokyo Department of Information Physics and Computing Tokyo113-8656 Japan

In this paper, we study the problem of resilient consensus for a multi-agent network, where some adversarial nodes attempt to prevent consensus of nonfaulty nodes by transmitting faulty values. Our approach is based on that of the so-called mean subsequence reduced (MSR) algorithm with a special emphasis on its use in agents capable to communicate with multi-hop neighbors. The MSR algorithm provides an effective technique for agents to achieve resilient consensus if the multi-agent network satisfies certain connectivity requirements. Our analysis highlights that for maintaining the same level of resilience against adversarial nodes, such network connectivity requirements can be relaxed by increasing the number of relay hops. In particular, we characterize tight network structures for our algorithm to succeed and propose a novel notion of graph robustness with multi-hop communication. Moreover, we analyze the multi-hop W-MSR algorithm with delays in communication since messages from multi-hop neighbors may require different numbers of time steps for their transmissions. Numerical examples are also presented to verify the efficacy of the proposed method. © 1963-2012 IEEE.

关键词： consensus algorithm

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Multi-Objective Feature Selection algorithm Based on Mutual Information and NSGA-II 11th

Multi-Objective Feature Selection Algorithm Based on Mutual ...

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11th Frontier Academic Forum of Electrical Engineering, FAFEE 2024

作者： Li, Erchao Sheng, Longwei School of Electrical Engineering and Information Engineering Lanzhou University of Technology Lanzhou730050 China

ISBN: (纸本)9789819788279

Feature Selection (FS) is a crucial data preparation method used in the classification process. FS challenges have been successfully solved with a variety of evolutionary computational approaches. Aiming at the shortcomings of the existing wrapping feature selection algorithm with low search efficiency, and considering the complementary advantages of the filtering method and the wrapping method in feature selection, this paper proposes a multi-objective feature selection algorithm that combines mutual information and NSGA-II (MOFS-MIGA). Firstly, in order to form a good frontier in the initial generation and improve the convergence speed of the algorithm, the symmetric uncertainty (SU) between each feature and class label is calculated based on the mutual information, and the population is initialized based on this uncertainty;secondly, a new crossover and mutation method is suggested to enhance the directionality and convergence of NSGA-II. Ultimately, the suggested algorithm achieves a set of converged and well-distributed solutions on the majority of datasets and has good feature selection performance. This is demonstrated by the experimental results, which compare the algorithm's performance to five traditional multi-objective feature selection algorithms on nine datasets of varying dimensions. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： consensus algorithm

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Distributed Frobenius-Norm Finite Memory Interacting Multiple Model Estimation for Mobile Robot Localization

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IEEE ACCESS 2022年 10卷 124193-124205页

作者： Suh, Duckhyun Lee, Dong Kyu Pak, Jung Min Ahn, Choon Ki Korea Univ Sch Elect Engn Seoul 02841 South Korea Wonkwang Univ Dept Elect Engn Iksan Si 54538 Jeollabuk Do South Korea

This paper proposes a novel state estimation algorithm, called the distributed Frobenius-norm finite memory interacting multiple model (DFFM-IMM) estimation algorithm, for mobile robot localization in wireless sensor networks (WSNs). The proposed algorithm involves finite memory estimation based on recent finite measurements;such estimation facilitates robust localization in cases of missing measurements and robot kidnapping. Furthermore, the proposed algorithm employs IMM, which facilitates accurate localization if a mobile robot abruptly changes its speed and course. Notably, average-consensus-based distributed processing renders the proposed DFFM-IMM algorithm computationally efficient, and hence, real-time processing for very short sampling times of the WSN is possible. The proposed algorithm's performance is demonstrated by comparing it with a centralized Frobenius-norm finite memory IMM (CFFM-IMM) estimation algorithm and a localization algorithm on the basis of simulations and experiments.

关键词： Location awareness Wireless sensor networks Distributed processing Estimation consensus algorithm Robot sensing systems Real-time systems Distributed localization finite memory estimation interacting multiple-model mobile robot wireless sensor networks

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Research on the Causal Forest algorithm Based on Factor Space Theory 6th

Research on the Causal Forest Algorithm Based on Factor Spac...

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6th IFIP TC 12 International Conference on Intelligence Science, ICIS 2024

作者： Zeng, Fanhui Lin, Kaile Liu, Xiaotong Zhang, Pengxue Ren, Sixing College of Science Liaoning Technical University Fuxin123000 China Institute of Intelligence Engineering and Mathematics Liaoning Technical University Fuxin123000 China

ISBN: (纸本)9783031712524

In order to solve the problems in classification, improve the classification accuracy and generalization ability of a single classifier, based on the degree of determination of factor space theory, a causal tree algorithm is proposed;Ensemble multiple causal trees, provide a definition of factor importance, obtain advantage factors, set importance thresholds in numerical experiments to reduce the set of condition factors, randomly select factors and then obtain the causal forest algorithm. Experimental comparison was conducted on the classification datasets of the UCI database to comprehensively evaluate the causal tree algorithm, causal forest algorithm, SVM algorithm and random forest algorithm. The experimental results showed that the causal forest algorithm performed well in terms of accuracy, precision, recall, F1 value, and AUC indexes, especially on the Vote and Cancer datasets, with good predictive and generalization abilities. The research conclusion expands the theoretical and applied research of factor space in data mining. © IFIP International Federation for Information Processing 2025.

关键词： consensus algorithm

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Research on Face Recognition algorithm based on ELPP and fuzzy K Nearest Neighbor algorithm 16

Research on Face Recognition Algorithm based on ELPP and fuz...

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2024 16th International Conference on Graphics and Image Processing, ICGIP 2024

作者： Ge, Hongmei Song, Aibo School of Computer Science and Engineering Southeast University Nanjing211189 China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

The essence of locality preserving projections (LPP) is a classic and linear approximation of Laplacian eigenmaps (LE), which can effectively extract the local manifold structure information. However, LPP is an unsupervised learning method, which cannot make good use of the category information of samples. Therefore, by introducing adjustable factors, the extended locality preserving projections (ELPP) method was formed considering sample categories while preserving local information. In addition, the performance of traditional KNN classifiers varies significantly due to the selection of value. However, the fuzzy k-nearest neighbor (FKNN) algorithm adds the fuzzy factor to make the generalization performance stronger. Therefore, this paper combines fuzzy K-nearest neighbor algorithm with ELPP to form FELPP learning model. Before using FELPP model, it is necessary to find out the optimal value of the tunable factor. Then the superiority of FELPP algorithm is proved by the test results on the Yale, ORL, and AR face database. © 2025 SPIE.

关键词： consensus algorithm

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Thoughts on the Big Data Parallel Clustering algorithm Based on the Mining of Community Maximal Classes

Thoughts on the Big Data Parallel Clustering Algorithm Based...

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2025 Asia-Europe Conference on Cybersecurity, Internet of Things and Soft Computing, CITSC 2025

作者： Mao, Hezheng Nanjing Tech University Nanjing China

ISBN: (纸本)9798331504205

In order to accurately and quickly find the network structure in big data, this paper proposes a big data clustering algorithm based on community maximal classes. To address the time consumption caused by the uncertainty of initial nodes and the calculation of the fitness function, local key nodes are introduced and the fitness formula is improved to reduce the time consumption. For the formation of the initial community, the concept of maximal clique is introduced. By analyzing the characteristics of maximal cliques, it is concluded that the core category of the community is composed of maximal cliques. Meanwhile, a method to obtain local core categories through the discovery of maximal cliques is proposed, and a parallel strategy for the maximal clique discovery algorithm is put forward. Then, the parallel strategy of the whole algorithm is proposed and experiments are conducted on real datasets. The experimental results prove that the algorithm proposed in this paper is feasible and effective, and is applicable to the discovery of network structures in large-scale data. © 2025 IEEE.

关键词： consensus algorithm

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Online b-Matching with Stochastic Rewards 50th

Online b-Matching with Stochastic Rewards

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50th International Conference on Current Trends in Theory and Practice of Computer Science, SOFSEM 2025

作者： Albers, Susanne Schubert, Sebastian Department of Computer Science Technical University of Munich Garching Germany

ISBN: (纸本)9783031826696

The b-matching problem is an allocation problem where the vertices on the left-hand side of a bipartite graph, referred to as servers, may be matched multiple times. In the setting with stochastic rewards, an assignment between an incoming request and a server turns into a match with a given success probability. Mehta and Panigrahi (FOCS 2012) introduced online bipartite matching with stochastic rewards, where each vertex may be matched once. The framework is equally interesting in graphs with vertex capacities. In Internet advertising, for instance, the advertisers seek successful matches with a large number of users. We develop (tight) upper and lower bounds on the competitive ratio of deterministic and randomized online algorithms, for b-matching with stochastic rewards. Our bounds hold for both offline benchmarks considered in the literature. As in prior work, we first consider vanishing probabilities. We show that no randomized online algorithm can achieve a competitive ratio greater than 1-1/e≈0.632, even for identical vanishing probabilities and arbitrary uniform server capacities. Furthermore, we conduct a primal-dual analysis of the deterministic StochasticBalance algorithm. We prove that it achieves a competitive ratio of 1-1/e, as server capacities increase, for arbitrary heterogeneous non-vanishing edge probabilities. This performance guarantee holds in a general setting where servers have individual capacities and for the vertex-weighted problem extension. To the best of our knowledge, this is the first result for StochasticBalance with arbitrary non-vanishing probabilities. We remark that our impossibility result implies in particular that, for the AdWords problem, no online algorithm can be better than (1-1/e)-competitive in the setting with stochastic rewards. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： consensus algorithm

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Blockchain Security: A Survey of Techniques and Research Directions

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2022年第4期15卷 2490-2510页

作者： Leng, Jiewu Zhou, Man Zhao, J. Leon Huang, Yongfeng Bian, Yiyang Guangdong Univ Technol State Key Lab Precis Elect Mfg Technol & Equipmen Guangzhou 510006 Guangdong Peoples R China City Univ Hong Kong Dept Informat Syst Hong Kong Peoples R China Chinese Univ Hong Kong Sch Management & Econ Shenzhen 518172 Peoples R China Jiangsu Univ Technol Sch Comp Engn Changzhou 213000 Jiangsu Peoples R China Nanjing Univ Dept Informat Management & Informat Syst Nanjing 210000 Jiangsu Peoples R China

Blockchain, an emerging paradigm of secure and shareable computing, is a systematic integration of 1) chain structure for data verification and storage, 2) distributed consensus algorithms for generating and updating data, 3) cryptographic techniques for guaranteeing data transmission and access security, and 4) automated smart contracts for data programming and operations. However, the progress and promotion of Blockchain have been seriously impeded by various security issues in blockchain-based applications. Furthermore, previous research on blockchain security has been mostly technical, overlooking considerable business, organizational, and operational issues. To address this research gap from the perspective of information systems, we review blockchain security research in three levels, namely, the process level, the data level, and the infrastructure level, which we refer to as the PDI model of blockchain security. In this survey, we examine the state of blockchain security in the literature. Based on the insights obtained from this initial analysis, we then suggest future directions of research in blockchain security, shedding light on urgent business and industrial concerns in related computing disciplines.

关键词： Security Blockchain Smart contracts Business Computer architecture Organizations Encryption Blockchain security process security data security consensus algorithm smart contract

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Nearly-Optimal algorithm for Non-Clairvoyant Service with Delay 42

Nearly-Optimal Algorithm for Non-Clairvoyant Service with De...

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42nd International Symposium on Theoretical Aspects of Computer Science, STACS 2025

作者： Touitou, Noam Tel Aviv Israel

ISBN: (纸本)9783959773652

We consider the online service with delay problem, in which a server traverses a metric space to serve requests that arrive over time. Requests gather individual delay cost while awaiting service, penalizing service latency;an algorithm seeks to minimize both its movement cost and the total delay cost. algorithms for the problem (on general metric spaces) are only known for the clairvoyant model, where the algorithm knows future delay cost in advance (e.g., Azar et al., STOC’17;Azar and Touitou, FOCS’19;Touitou, STOC’23). However, in the non-clairvoyant setting, only negative results are known: where n is the size of the metric space and m is the number of requests, there are lower bounds of Ω(√n) and Ω(√m) on competitiveness (Azar et al., STOC’17), that hold even for randomized algorithms (Le et al., SODA’23). In this paper, we present the first algorithm for non-clairvoyant online service with delay. Our algorithm is deterministic and O(min{√n log n, √m log m )-competitive;combined with the lower bounds of Azar et al., this settles the correct competitive ratio for the problem up to logarithmic factors, in terms of both n and m. © Noam Touitou.

关键词： consensus algorithm

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