Volume 13, Issue 4 (12-2021)                   itrc 2021, 13(4): 18-27 | Back to browse issues page


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Rahiimi F. A Distributed Optimization Approach for Multi-Agent Systems over Delaying Networks. itrc. 2021; 13 (4) :18-27
URL: http://ijict.itrc.ac.ir/article-1-495-en.html
Department of Electrical Engineering Sahand University of Technology Tabriz, Iran. , fa_rahimi@sut.ac.ir
Abstract:   (328 Views)
This paper investigates a novel method to solve distributed optimization problems in the presence of communication delays between the networked agents that cooperate together to find an optimal solution of a global cost function composed of local ones. In the problem of distributed optimization in a network of multi-agent because of existing phenomena such as communication delay, deriving approaches having appropriate performance so that the states of all agents converge to the same value always has been a substantial challenge. Delay-dependent conditions in the form of linear matrix inequities are derived to analyze the convergence of the introduced scheme to the optimal solution. It is demonstrated that the maximum allowable time delay in the network and convergence rate of the optimization procedure are increased by the suggested strategy. Finally, comparative simulation results are considered to illustrate the superior performance of the introduced scheme compared to a rival one in the literature.
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References
1. S. Yang, Q. Liu, and J. Wang, "A multi-agent system with a proportional-integral protocol for distributed constrained optimization," IEEE Transactions on Automatic Control, vol. 62, no. 7, pp. 3461-3467, 2016. [2] S. Gao and C. W. de Silva, "Estimation distribution algorithms on constrained optimization problems," Applied Mathematics and Computation, vol. 339, pp. 323-345, 2018. [3] R. Van Parys and G. Pipeleers, "Distributed MPC for multi-vehicle systems moving in formation," Robotics and Autonomous Systems, vol. 97, pp. 144-152, 2017. [4] F. Chen and W. Ren, "On the control of multi-agent systems: A survey," Foundations and Trends® in Systems and Control, vol. 6, no. 4, pp. 339-499, 2019. K. Sakurama and M. Miura, "Distributed constraint optimization on networked multi-agent systems," Applied Mathematics and Computation, vol. 292, pp. 272-281, 2017. [6] F. Rahimi, "Distributed Control for Nonlinear Multi-Agent Systems Subject to Communication Delays and Cyber-Attacks: Applied to One-Link Manipulators," in 2021 9th RSI International Conference on Robotics and Mechatronics (ICRoM), 2021: IEEE, pp. 24-29. [7] A. Nedic, A. Ozdaglar, and P. A. Parrilo, "Constrained consensus and optimization in multi-agent networks," IEEE Transactions on Automatic Control, vol. 55, no. 4, pp. 922-938, 2010. [8] M. Zhu and S. Martínez, "On distributed convex optimization under inequality and equality constraints," IEEE Transactions on Automatic Control, vol. 57, no. 1, pp. 151-164, 2011. [9] D. Yuan, S. Xu, B. Zhang, and L. Rong, "Distributed primal-dual stochastic subgradient algorithms for multi‐agent optimization under inequality constraints," International Journal of Robust and Nonlinear Control, vol. 23, no. 16, pp. 1846-1868, 2013. [10] Y. Lou, G. Shi, K. H. Johansson, and Y. Hong, "Approximate projected consensus for convex intersection computation: Convergence analysis and critical error angle," IEEE Transactions on Automatic Control, vol. 59, no. 7, pp. 1722-1736, 2014. [11] Z. Qiu, S. Liu, and L. Xie, "Necessary and sufficient conditions for distributed constrained optimal consensus under bounded input," International Journal of Robust and Nonlinear Control, vol. 28, no. 6, pp. 2619-2635, 2018. [12] Z. Qiu, S. Liu, and L. Xie, "Distributed constrained optimal consensus of multi-agent systems," Automatica, vol. 68, pp. 209-215, 2016. [13] Q. Liu and J. Wang, "A second-order multi-agent network for bound-constrained distributed optimization," IEEE Transactions on Automatic Control, vol. 60, no. 12, pp. 3310-3315, 2015. [14] S. S. Kia, J. Cortés, and S. Martínez, "Distributed convex optimization via continuous-time coordination algorithms with discrete-time communication," Automatica, vol. 55, pp. 254-264, 2015. [15] G. N. Droge, "Behavior-based model predictive control for networked multi-agent systems," Georgia Institute of Technology, 2014. [16] F. Rahimi and R. M. Esfanjani, "Distributed predictive control for formation of networked mobile robots," in 2018 6th RSI International Conference on Robotics and Mechatronics (IcRoM), 2018: IEEE, pp. 70-75. [17] T. Weng, L. Wang, Z. She, and Q. Liang, "Distributed optimization with closed convex set for multi-agent networks over directed graphs," Journal of the Franklin Institute, vol. 356, no. 2, pp. 883-893, 2019. [18] Z. Guo and G. Chen, "Distributed zero‐gradient‐sum algorithm for convex optimization with time‐varying communication delays and switching networks," International Journal of Robust and Nonlinear Control, vol. 28, no. 16, pp. 4900-4915, 2018. [19] T. Yang et al., "A survey of distributed optimization," Annual Reviews in Control, vol. 47, pp. 278-305, 2019. [20] F. Rahimi and H. Rezaei, "A Distributed Fault Estimation Approach for a Class of Continuous-time Nonlinear Networked Systems Subject to Communication Delays," IEEE Control Systems Letters, 2021. [21] J. Li, G. Chen, Z. Dong, and Z. Wu, "Distributed mirror descent method for multi-agent optimization with delay," Neurocomputing, vol. 177, pp. 643-650, 2016. [22] H. Wang, X. Liao, T. Huang, and C. Li, "Cooperative distributed optimization in multiagent networks with delays," IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 45, no. 2, pp. 363-369, 2014. [23] T. Hatanaka, N. Chopra, T. Ishizaki, and N. Li, "Passivity-based distributed optimization with communication delays using PI consensus algorithm," IEEE Transactions on Automatic Control, vol. 63, no. 12, pp. 4421-4428, 2018. S. Yang, Q. Liu, and J. Wang, "Distributed optimization based on a multiagent system in the presence of communication delays," IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 47, no. 5, pp. 717-728, 2016. [25] P. Lin, W. Ren, and Y. Song, "Distributed multi-agent optimization subject to nonidentical constraints and communication delays," Automatica, vol. 65, pp. 120-131, 2016. [26] J. Yan, H. Yu, and X. Xia, "Distributed optimization of multi-agent systems with delayed sampled-data," Neurocomputing, vol. 296, pp. 100-108, 2018. [27] M. Mesbahi and M. Egerstedt, Graph theoretic methods in multiagent networks. Princeton University Press, 2010. [28] M. S. Bazaraa, H. D. Sherali, and C. M. Shetty, Nonlinear programming: theory and algorithms. John Wiley & Sons, 2013. [29] A. Nedic and A. Ozdaglar, "Distributed subgradient methods for multi-agent optimization," IEEE Transactions on Automatic Control, vol. 54, no. 1, pp. 48-61, 2009. [30] G. Scutari, D. P. Palomar, F. Facchinei, and J.-S. Pang, "Convex optimization, game theory, and variational inequality theory," IEEE Signal Processing Magazine, vol. 27, no. 3, pp. 35-49, 2010. [31] P. Giselsson, M. D. Doan, T. Keviczky, B. De Schutter, and A. Rantzer, "Accelerated gradient methods and dual decomposition in distributed model predictive control," Automatica, vol. 49, no. 3, pp. 829-833, 2013. [32] S. Boyd, L. El Ghaoui, E. Feron, and V. Balakrishnan, Linear matrix inequalities in system and control theory. SIAM, 1994. [33] K. Gu, J. Chen, and V. L. Kharitonov, Stability of time-delay systems. Springer Science & Business Media, 2003. [34] P. Park, J. W. Ko, and C. Jeong, "Reciprocally convex approach to stability of systems with time-varying delays," Automatica, vol. 47, no. 1, pp. 235-238, 2011. [35] F. Rahimi and R. M. Esfanjani, "A Distributed Dual Decomposition Optimization Approach for Coordination of Networked Mobile Robots with Communication Delay," in 2021 9th RSI International Conference on Robotics and Mechatronics (ICRoM), 2021: IEEE, pp. 18-23. [36] J. P. Queralta et al., "Collaborative multi-robot systems for search and rescue: Coordination and perception," arXiv preprint arXiv:2008.12610, 2020. [37] F. Rahimi and H. Rezaei, "An event-triggered recursive state estimation approach for time-varying nonlinear complex networks with quantization effects," Neurocomputing, vol. 426, pp. 104-113, 2021.

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