Optimal design for manipulation of random consensus over discrete information in networked systems |
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| Authors: | Sidney Roberto Carvalho Ubirajara Franco Moreno |
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| Institution: | 1. School of Automation, Nanjing University of Science &Technology, No. 200 Xiaolingwei, Nanjing, China;2. School of Science, Nanjing University of Science &Technology, No. 200 Xiaolingwei, Nanjing, China;3. Shanghai Institute of Mechanical and Electrical Engineering, No.421 Guilin Road, Shanghai, China;1. Beijing Institute of Control Engineering, China Academy of Space Technology, Beijing 100080, China;2. Beijing Institute of Spacecraft System Engineering, China Academy of Space Technology, Beijing 100094, China;3. School of Aeronautics and Astronautic, Sun Yat-sen University, Guangzhou 510006, China;1. Department of Mechanical Engineering, Graduate School of Engineering, University of Hyogo, 2167, Shosha, Himeji, Hyogo 671-2280, Japan;2. Okayama University, 3-1-1, Tsushima-Naka, Kitaku, Okayama-shi, 700-0082, Japan |
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| Abstract: | In distributed and cooperative systems, the network structure is determinant to the success of the strategy adopted to solve complex tasks. Those systems are primarily governed by consensus protocols whose convergence is intrinsically dependent on the network topology. Most of the consensus algorithms deal with continuous values and perform average-based strategies to reach cohesion over the exchanged information. However, many problems demand distributed consensus over countable values, that cannot be handled by traditional protocols. In such a context, this work presents an approach based on semidefinite programming to design the optimal weights of a network adjacency matrix, in order to control the convergence of a distributed random consensus protocol for variables at the discrete-space domain, based on the voter model. As a second contribution, this work uses Markov theory and the biological inspiration of epidemics to find out a dynamical spreading model that can predict the information diffusion over this discrete consensus protocol. Also, convergence properties and equilibrium points of the proposed model are presented regarding the network topology. Finally, extensive numerical simulations evaluate the effectiveness of the proposed consensus algorithm, its spreading model, and the approach for optimal weight design. |
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