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Identifying the Most Influential Driver Nodes for Pinning Control of Multi-Agent Systems with Time-Varying Topology (2405.18712v1)

Published 29 May 2024 in eess.SY and cs.SY

Abstract: Identifying the most influential driver nodes to guarantee the fastest synchronization speed is a key topic in pinning control of multi-agent systems. This paper develops a methodology to find the most influential pinning nodes under time-varying topologies. First, we provide the pinning control synchronization conditions of multi-agent systems. Second, a method is proposed to identify the best driver nodes that can guarantee the fastest synchronization speed under periodically switched systems. We show that the determination of the best driver nodes is independent of the system matrix under certain conditions. Finally, we develop a method to estimate the switching frequency threshold that can make the selected best driver nodes remain the same as the average system. Numerical simulations reveal the feasibility of these methods.

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References (19)
  1. M. Herrera, M. Pérez-Hernández, A. Kumar Parlikad, and J. Izquierdo, “Multi-agent systems and complex networks: Review and applications in systems engineering,” Processes, vol. 8, no. 3, p. 312, 2020.
  2. K. Ayepah, M. Sun, and Q. Jia, “Event-triggered synchronization of switching dynamical networks with periodic sampling,” IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 68, no. 6, pp. 2172–2176, 2021.
  3. J. Wang and X. Liu, “Cluster synchronization for multi-weighted and directed complex networks via pinning control,” IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 69, no. 3, pp. 1347–1351, 2022.
  4. Z. Qiu, T. Fan, M. Li, and L. Lü, “Identifying vital nodes by achlioptas process,” New Journal of Physics, vol. 23, no. 3, p. 033036, 2021.
  5. Y. Li, S. Zhang, T. Xu, M. Zhu, and Z. He, “Evaluation of critical node groups in cyber-physical power systems based on pinning control theory,” IEEE Access, vol. 10, pp. 48 936–48 947, 2022.
  6. M. Jalili, O. A. Sichani, and X. Yu, “Optimal pinning controllability of complex networks: Dependence on network structure,” Physical Review E, vol. 91, no. 1, p. 012803, 2015.
  7. M.-Y. Zhou, Z. Zhuo, H. Liao, Z.-Q. Fu, and S.-M. Cai, “Enhancing speed of pinning synchronizability: low-degree nodes with high feedback gains,” scientific Reports, vol. 5, no. 1, p. 17459, 2015.
  8. Y. Orouskhani, M. Jalili, and X. Yu, “Optimizing dynamical network structure for pinning control,” Scientific reports, vol. 6, no. 1, p. 24252, 2016.
  9. A. M. Amani, M. Jalili, X. Yu, and L. Stone, “Finding the most influential nodes in pinning controllability of complex networks,” IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 64, no. 6, pp. 685–689, 2017.
  10. ——, “Controllability of complex networks: Choosing the best driver set,” Physical Review E, vol. 98, no. 3, p. 030302, 2018.
  11. M.-Y. Zhou, R.-Q. Xu, X.-Y. Li, and H. Liao, “Identifying influential nodes to enlarge the coupling range of pinning controllability,” Journal of Statistical Mechanics: Theory and Experiment, vol. 2020, no. 9, p. 093401, 2020.
  12. O. P. Mahela, M. Khosravy, N. Gupta, B. Khan, H. H. Alhelou, R. Mahla, N. Patel, and P. Siano, “Comprehensive overview of multi-agent systems for controlling smart grids,” CSEE Journal of Power and Energy Systems, vol. 8, no. 1, pp. 115–131, 2020.
  13. D. S. Drew, “Multi-agent systems for search and rescue applications,” Current Robotics Reports, vol. 2, pp. 189–200, 2021.
  14. S. Koohi-Fayegh and M. A. Rosen, “A review of energy storage types, applications and recent developments,” Journal of Energy Storage, vol. 27, p. 101047, 2020.
  15. Y. Chen, Z. Wang, J. Hu, and Q.-L. Han, “Synchronization control for discrete-time-delayed dynamical networks with switching topology under actuator saturations,” IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 5, pp. 2040–2053, 2020.
  16. G. Zhang, X. Yu, Z. Chen, M. Jalili, and A. M. Amani, “Pinning control of multi-agent systems with periodical switching topology,” in 2022 IEEE International Conference on Industrial Technology (ICIT).   IEEE, 2022, pp. 1–8.
  17. W. Zou, C. Zhou, J. Guo, and Z. Xiang, “Global adaptive leader-following consensus for second-order nonlinear multiagent systems with switching topologies,” IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 68, no. 2, pp. 702–706, 2021.
  18. H. Kim, H. Shim, J. Back, and J. H. Seo, “Consensus of output-coupled linear multi-agent systems under fast switching network: Averaging approach,” Automatica, vol. 49, no. 1, pp. 267–272, 2013.
  19. D. J. Stilwell, E. M. Bollt, and D. G. Roberson, “Sufficient conditions for fast switching synchronization in time-varying network topologies,” SIAM Journal on Applied Dynamical Systems, vol. 5, no. 1, pp. 140–156, 2006.

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