Distributed coordination on state-dependent fuzzy graphs |
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| Authors: | Mojeed O Oyedeji Magdi S Mahmoud Yuanqing Xia |
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| Institution: | 1. Systems Engineering Department, KFUPM, Dhahran 31261, Saudi Arabia;2. School of Automation, Beijing Institute of Technology, Beijing, China;1. School of Mathematics and Statistics, Shandong Normal University, Jinan 250014, PR China;2. Center for Control and Engineering Computation, Shandong Normal University, Jinan 250014, PR China;3. School of Mathematics and Computer Science, Yunnan Minzu University, Kunming 650500, PR China;4. Department of Applied Mathematics, University of Waterloo, Waterloo, ON N2L 3G1, Canada;5. School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Perth, WA 6845, Australia;1. School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China;2. School of Automation, Beijing Institute of Technology, Beijing 100081, China;3. School of Information and Communication Engineering, Beijing Information Science and Technology University, Beijing 100192, China;1. The Key Laboratory of Autonomous Systems and Network Control, Ministry of Education, School of Automatic Science and Engineering, South China University of Technology, Guangzhou 510641, China;2. Henan Engineering Laboratory for Big Data Statistical Analysis and Optimal Control, School of Mathematics and Information Sciences, Henan Normal University, Xinxiang, Henan 453007, China;1. Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China;2. School of Mathematical Sciences, Tongji University, Shanghai 200092, China;3. Department of Mathematics, University of Michigan, Ann Arbor, MI 48109, USA;1. Communication Engineering at Al-Hussein bin Talal University, Ma’an, Jordan;2. Electrical Engineering at Jordan University of Science and Technology, Irbid, Jordan |
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| Abstract: | Multiagent systems are increasingly becoming popular among researchers spanning multiple fields of study. However, existing studies only models communication interaction between agents as either fixed or switching topologies described by crisp graphs supported by algebraic graph theories. In this paper, we propose an alternative approach to describing agent interactions using fuzzy graphs. Our approach is aimed at opening up new research avenues and defining new problems in coordination control especially in terms of dynamics between agents’ states, graph topologies and coordination objectives. This paper studies distributed coordination on fuzzy graphs where the edge-weights modeling network topologies are dependent on the states of the agents in the network. In hindsight, the network weights are adjustable based on the situational state of the agents. First, we introduce the concept of fuzzy graphs and give some distinguishing features from the crisp or fixed graphs. Next, we provide some membership functions to define the state-dependent weights and finally we use some simulations to demonstrate the convergence of the proposed consensus algorithms especially for cases where the agents are subject to system failures. |
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