This paper is concerned with the sampled-data-based consensus problem of heterogeneous multiagent systems under directed graph topology with communication failure. The heterogeneous multiagent system consists of first-order and second-order integrators. Consensus of the heterogeneous multiagent system may not be guaranteed if the communication failure always happens. However, if the frequency and the length of the communication failure satisfy certain conditions, consensus of the considered system can be reached. In particular, we introduce the concepts of communication failure frequency and communication failure length. Then, with the help of the switching technique and the Lyapunov stability theory, sufficient conditions are derived in terms of linear matrix inequalities, which guarantees that the heterogeneous multiagent system not only achieves consensus but also maintains a desired L -gain performance. A simulation example is given to show the effectiveness of the proposed method in this paper.
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http://dx.doi.org/10.1109/TCYB.2016.2550612 | DOI Listing |
Neural Netw
December 2024
Laboratory of Speech and Intelligent Information Processing, Institute of Acoustics, CAS, Beijing, China; University of Chinese Academy of Sciences, Beijing, China. Electronic address:
In multi-agent cooperative tasks, the presence of heterogeneous agents is familiar. Compared to cooperation among homogeneous agents, collaboration requires considering the best-suited sub-tasks for each agent. However, the operation of multi-agent systems often involves a large amount of complex interaction information, making it more challenging to learn heterogeneous strategies.
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December 2024
School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China. Electronic address:
Denial-of-service (DoS) attacks and antagonistic interactions may exist in complex networks, which will destroy cooperative communication between agents and thus cannot realize collaborative tasks. Therefore, this paper studies time-varying formation tracking (TVFT) of heterogeneous multi-agent systems (HMASs) with DoS attacks and cooperative-antagonistic interactions. It aims to ensure system communication connectivity and allow followers to achieve distributed secure bipartite TVFT.
View Article and Find Full Text PDFMol Ther
December 2024
State Key Laboratory of Medicinal Chemical Biology, College of Life Sciences, and Frontiers Science Center for Cell Responses, Nankai University, Tianjin 300071, China; Beijing Institute of Biological Products Company Limited and CNBG-Nankai University Joint Research and Development Center, Beijing 100176, China; Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, Shanghai, China. Electronic address:
Oncolytic viruses have been considered promising cancer immunotherapies. However, oncovirotherapy agents impart durable responses in only a subset of cancer patients. Thus, exploring the cellular and molecular mechanisms underlying the heterogeneous responses in patients can provide guidance to develop more effective oncolytic virus therapies.
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November 2024
The School of Electrical and Mechanical Engineering, University of Adelaide, Adelaide, SA, 5005, Australia. Electronic address:
This paper focuses on the design of event-triggered observer-based heterogeneous memory controllers for leader-following multi-agent systems with time-varying topology. In order to save limited on-board resources, a novel adaptive event-triggered strategy based on the nonlinear transformation law of the estimation error is proposed in this paper, which can effectively reduce some unnecessary data transmission due to small fluctuations after the estimation error converges. Then, a more general topology structure described by an interval type-2 fuzzy model is adopted, which contains both nonlinear time-varying law and uncertain parameters.
View Article and Find Full Text PDFSensors (Basel)
November 2024
Graduate School of Informatics, Nagoya University, Furo-cho, Chikusa-Ward, Nagoya 464-8601, Japan.
The accurate prediction of vehicle behavior is crucial for autonomous driving systems, impacting their safety and efficiency in complex urban environments. To address the challenge of multi-agent trajectory prediction, we propose a novel model integrating multiple input modalities, including historical trajectories, map data, vehicle features, and interaction information. Our approach employs a Conditional Variational Autoencoder (CVAE) framework with a decoder that predicts control actions using the Gaussian Mixture Model (GMM) and then converts these actions into dynamically feasible trajectories through a bicycle model.
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